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<> machine learning (57)

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

Novel computational methods for promoter identification and analysis

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B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

SFB Group graduated its third PhD student - Dr. Ibrahim Alabdulmohsin!

1 min read ·
Congratulates the SFB group on graduating its third Ph.D. student, Dr. Ibrahim Alabdulmohsin.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

Computers excel in chemistry class

1 min read ·
Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

1 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

CBRC congratulates Aryam Alotaibi a SRSI student on winning the top paper Award

1 min read ·
Aryam Alotaibi a SRSI student at CBRC wins the top paper Award titled "Computational method to predict drug-disease associations using machine learning and graph embedding."

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

1 min read ·
KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

Zhenzhen Zou

Zhenzhen Zou worked as a ​Researcher at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Zhenzhen's research interests included Bioinformatics and Machine learning.

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Siyuan Chen

M.S. Student, Computer Science
Siyuan Chen is an M.S./Ph.D. candidate in Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Before joining KAUST, Chen obtained a bachelor's degree from the School of Communication and Information Engineering, UESTC, China. Research Interests Siyuan's research interests lie in bioinformatics, computational biology, machine learning, and big data. He intends to work at the intersection between computer science and biology and develop algorithms so as to solve the key problems in medicine as well as in biology.

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Sakhaa Alsaedi

Ph.D. Student, Computer Science
​Sakhaa Al-Saedi completed her bachelor's degree in Computer Science in 2016 at Taibah University. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah, before starting her master's degree at KAUST in 2017. She is currently working with the Bio-Ontology Research Group (BORG) of Professor Robert Hoehndorf on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Ramzan Umarov

Ph.D. Student, Computer Science
Ramzan is a PhD candidate, working on solving biological problems by using applied machine learning with focus on deep learning. He has developed deep learning based methods to solve bioinformatics problems achieving state-of-the-art performance, focusing on various aspects of gene regulation. Umarov obtained his Master degree from Imperial College London, Advanced Computing course. Research Interests The main research interest of Ramzan Umarov is applied machine learning especially Deep Learning. Professional Profile 2011-2012, Programmer, Softberry, Mount Kisko, NY, USA 2013-2014, Programmer

Qiming Fang

Prof. Fang is a visiting professor from Hangzhou Dianzi University, China at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Prof. Fang's research interests include Bioinformatics and Machine learning.

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Mona Alshahrani

Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Majed Alzahrani

Majed Alzahrani obtained PhD degree in Computer Science from King Abdullah University of Science and Technology in 2017. His PhD thesis supervisor was Professor Xin Gao. After graduating, he joined Saudi Aramco as a data scientist in its Drilling Data Science unit. Working in one of the largest companies in the world is a dream come true for many, for Majed Alzahrani, things couldn't be better. Research Interest Majed's research interests were in machine learning, structural bioinformatics, synthetic biology.

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Lizhong Ding

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Lizhong Ding worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Lizhong is a research scientist at the Inception Institute of Artificial Intelligence. Research Interests Machine learning theory Impact of approximation on learning algorithms, especially discrepancy bounds between approximate and accurate learning, for kernel learning and deep learning Large scale machine learning Large scale matrix analysis/approximation Scalable model

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a

Cheng Wang

Ph.D. Student, Computer Science
Cheng Wang received his PhD in the Computer Science Program under the supervision of Professor Helmut Pottmann at the Visual Computing Center (VCC). He is now an Assistant Professor at the University of East Anglia in the United Kingdom Education and Early Career Cheng received his Bachelor's Degree in Automation from Tsinghua University, China in 2016. He joined KAUST to continue his studies and received his Master's Degree in Computer Science in 2018. Research Interest Cheng is mainly interested in Computer Graphics, Geometric Computing, and Machine Learning. Education Profile M.S., Computer

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.
<> bioinformatics (41)

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

Towards rational design of biosynthesis pathways

-

B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

-

B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Please see our publication page

1 min read ·
The SFB group has been very productive in both bioinformatics/computational biology and artificial intelligence.

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

1 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

CBRC congratulates Aryam Alotaibi a SRSI student on winning the top paper Award

1 min read ·
Aryam Alotaibi a SRSI student at CBRC wins the top paper Award titled "Computational method to predict drug-disease associations using machine learning and graph embedding."

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

1 min read ·
KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Zhenzhen Zou

Zhenzhen Zou worked as a ​Researcher at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Zhenzhen's research interests included Bioinformatics and Machine learning.

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Xuefeng Cui

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Xuefeng Cui worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2014-2016. Currently, Xuefenga is a Professor at Shandong University, China. Education Profile Ph.D. degree, University of Waterloo, Canada, Jan 2007 - April 2014 (except May-Aug, 2012 and May-Aug, 2013). Master's degree, University of Waterloo, Canada, Sep 2004 - Dec 2006. Bachelor's degree, University of Waterloo, Canada, Sep 2000 - Apr 2004. Columbia International College, Canada, Sep 1999 - Aug 2000

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Postdoctoral Research Fellow, Computer Science
Xiaopeng Xu is a Postdoctoral Fellow in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Postdoctoral Fellow program, Xiaopeng obtained Ph.D. and master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Shrihari Sonavane

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Shrihari Sonavane worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2013-2014. Shrihari received his Ph.D. degree from Jadavpur University, India. Research Interests Shrihari's research interests included Bioinformatics and computational biology. Selected Publications 1. Dey Sucharita, Pal Arumay, Guharoy Mainak, Sonavane Shrihari and Chakrabarti Pinak (2012) Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Qiming Fang

Prof. Fang is a visiting professor from Hangzhou Dianzi University, China at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Prof. Fang's research interests include Bioinformatics and Machine learning.

Peng Chen

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Chen worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2012-2014. Dr. Peng Chen is currently a Professor at the Institute of Health Sciences, Anhui University. Prior to Anhui University, he was an Associate Professor at the Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei Anhui, P.R.China. He received his Ph.D. degree in Pattern Recognition and Intelligent System from the University of Science and Technology of China, and Bachelor and

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Jordy Homing Lam

Jordy Homing Lam worked as a ​Research Associate at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST) during 2018-2019. Currently, Jordy is a Ph.D. student at USC. ​Jordy Homing Lam obtained his Master's Degree from Hong Kong University of Science and Technology. Research Interests Jordy's research interests included Bioinformatics, Computational Biology and Molecular Dynamics Simulation.

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang Li is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Haoyang majored in Bioinformatics and graduated from Jilin University (JLU), located in Changchun, Jilin, China. Research Interests Haoyang has a broad interest in developing AI models to address biological and healthcare problems. Recently, his research has focused on developing AI models for spatial biology, including spatial multi-omics, spatiotemporal omics, and pathological imaging, aimed at translational and clinical

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Christophe Van Neste

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Christophe Van Neste is a Postdoctoral Fellow under the supervision of Professor Xin Gao at the Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests ​My topic interests are centered around cancer and development, specifically related to the embryonal cancer neuroblastoma and how it links to the normal development of the sympathetic neuronal system. My focus there lies on replicative stress and secondary DNA structures such as G-quadruplexes and how that impacts development or cancer stem cell maintenance. My

Ahmed Abbas

Ahmed Abbas obtained his Ph.D. degree under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Ahmed is an Assistant Professor at Ain Shams University, Egypt. Research Interests Ahmed's research interests included Bioinformatics, NMR protein structure determination. Selected Publications 1. Carlo Cannistraci, Ahmed Abbas, and Xin Gao. (2015). “Median modified Wiener filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra”. Scientific Reports (Nature

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.
<> Computational biology (22)

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

CBRC goes to ECCB 2018

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The European Conference on Computational Biology (ECCB) is one of the most important conferences in the field of bioinformatics and computational biology. Held in a different European city annually, this year's conference will take place in the historic city of Athens, Greece on September 8-12, 2018.

Please see our publication page

1 min read ·
The SFB group has been very productive in both bioinformatics/computational biology and artificial intelligence.

Meet KAUST prospective student: Xiaopeng Xu

1 min read ·
Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Postdoctoral Research Fellow, Computer Science
Xiaopeng Xu is a Postdoctoral Fellow in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Postdoctoral Fellow program, Xiaopeng obtained Ph.D. and master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Siyuan Chen

M.S. Student, Computer Science
Siyuan Chen is an M.S./Ph.D. candidate in Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Before joining KAUST, Chen obtained a bachelor's degree from the School of Communication and Information Engineering, UESTC, China. Research Interests Siyuan's research interests lie in bioinformatics, computational biology, machine learning, and big data. He intends to work at the intersection between computer science and biology and develop algorithms so as to solve the key problems in medicine as well as in biology.

Shrihari Sonavane

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Shrihari Sonavane worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2013-2014. Shrihari received his Ph.D. degree from Jadavpur University, India. Research Interests Shrihari's research interests included Bioinformatics and computational biology. Selected Publications 1. Dey Sucharita, Pal Arumay, Guharoy Mainak, Sonavane Shrihari and Chakrabarti Pinak (2012) Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Peng Chen

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Chen worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2012-2014. Dr. Peng Chen is currently a Professor at the Institute of Health Sciences, Anhui University. Prior to Anhui University, he was an Associate Professor at the Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei Anhui, P.R.China. He received his Ph.D. degree in Pattern Recognition and Intelligent System from the University of Science and Technology of China, and Bachelor and

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

Jordy Homing Lam

Jordy Homing Lam worked as a ​Research Associate at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST) during 2018-2019. Currently, Jordy is a Ph.D. student at USC. ​Jordy Homing Lam obtained his Master's Degree from Hong Kong University of Science and Technology. Research Interests Jordy's research interests included Bioinformatics, Computational Biology and Molecular Dynamics Simulation.

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang Li is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Haoyang majored in Bioinformatics and graduated from Jilin University (JLU), located in Changchun, Jilin, China. Research Interests Haoyang has a broad interest in developing AI models to address biological and healthcare problems. Recently, his research has focused on developing AI models for spatial biology, including spatial multi-omics, spatiotemporal omics, and pathological imaging, aimed at translational and clinical
<> artificial intelligence (18)
Najmeddine Dhieb

Najmeddine Dhieb

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Professors discuss big data and human health in latest Sci-Café

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​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

The awesome scope of big data

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Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

AI reveals nature of RNA-protein interactions

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A deep learning tool could help in structure-based drug discovery.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

CEMSE Division startups in focus: Peregrine Genomics

1 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

AI that's built to save lives

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Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

KAUST Prospective Student: Siyuan Chen

1 min read ·
Siyuan Chen is a 22-year-old graduate who comes to KAUST from the School of Communication and Information Engineering, UESTC, China. Chen will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

How AI helps improve COVID-19 testing

1 min read ·
Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).

Meet KAUST prospective student: Xiaopeng Xu

1 min read ·
Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.

Safeguarding the right to be forgotten

1 min read ·
An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.

Former CBRC PhD Student Ibrahim Alabdulmohsin Highlighted for AI Contributions on Saudi National TV

1 min read ·
Former CBRC PhD student Dr. Ibrahim Alabdulmohsin, now a senior research scientist at Google DeepMind in Zurich, was featured by the Josoor Program on Saudi National TV. The program, meaning "Bridges" in Arabic, highlighted Dr. Alabdulmohsin's journey from his education at the University of Nebraska, Lincoln, and Stanford University to his leadership roles at Aramco's Advanced Analytics team. After graduating from KAUST under the supervision of Prof. Xin Gao, he joined DeepMind in 2019, contributing to groundbreaking AI research. The 24-minute episode explores his career and contributions to the field. Dr. Alabdulmohsin's commitment to advancing AI underscores his pivotal role in shaping the future of technology.

Sakhaa Alsaedi

Ph.D. Student, Computer Science
​Sakhaa Al-Saedi completed her bachelor's degree in Computer Science in 2016 at Taibah University. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah, before starting her master's degree at KAUST in 2017. She is currently working with the Bio-Ontology Research Group (BORG) of Professor Robert Hoehndorf on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling
<> Deep learning (14) <> Computer science (12)

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

AI reveals nature of RNA-protein interactions

1 min read ·
A deep learning tool could help in structure-based drug discovery.

Computers excel in chemistry class

1 min read ·
Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

Simulating evolution to understand a hidden switch

1 min read ·
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

Alumni in focus: Abdullah AlGziwi

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Abdullah AlGziwi, a visiting student under Professor Xin Gao's supervision, was recently awarded the “Second Place Grand Award” at the Saudi National Olympiad for Scientific Creativity. He presented his research project on “A Novel exosome-encapsulated-miRNA-based machine learning model for multiple cancer prediction and prognosis”.

Prof. Xin Gao assumes the position of CEMSE CS Program Chair

1 min read ·
Prof. Xin Gao has assumed the role of CEMSE CS Program Chair, marking a significant development for our academic community.

Former CBRC PhD Student Ibrahim Alabdulmohsin Highlighted for AI Contributions on Saudi National TV

1 min read ·
Former CBRC PhD student Dr. Ibrahim Alabdulmohsin, now a senior research scientist at Google DeepMind in Zurich, was featured by the Josoor Program on Saudi National TV. The program, meaning "Bridges" in Arabic, highlighted Dr. Alabdulmohsin's journey from his education at the University of Nebraska, Lincoln, and Stanford University to his leadership roles at Aramco's Advanced Analytics team. After graduating from KAUST under the supervision of Prof. Xin Gao, he joined DeepMind in 2019, contributing to groundbreaking AI research. The 24-minute episode explores his career and contributions to the field. Dr. Alabdulmohsin's commitment to advancing AI underscores his pivotal role in shaping the future of technology.
<> Computer Vision (8)

Abdelrahman Eldesokey

Postdoctoral Research Fellow
I am a Postdoctoral Fellow at KAUST, Saudi Arabia, specializing in Generative AI and conducting research on advanced topics in Generative and Large Language Models (LLMs). I bring over 11 years of combined academic and industrial experience in Computer Vision and Deep Learning across Sweden, Egypt, and Saudi Arabia.
Najmeddine Dhieb

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)
<> structural biology (7)

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

KAUST Research Conference 2023 on Computational Advances in Structural Biology

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B4 B5 A0215

Computational Bioscience Research Center (CBRC) is pleased to announce the KAUST Research Conference 2023 on Computational Advances in Structural Biology. The conference will be held from May 1-3, 2023 at the Auditorium between Building 4 & 5. The visualization of the structure of a molecule, organelle or larger entity is key to understanding its biological function. Thus, structural biology is an extremely powerful means of unraveling the fundaments of life, but also of diseases. Consequently, structural biology is at the heart of medical therapies, including drug design, and has enabled the

Student Poster Competition: The KAUST Research Conference on Computational Advances in Structural Biology

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B9 Hallway

The Computational Bioscience Research Center (CBRC) will be holding a student poster competition as part of its yearly conference. This poster competition is open to KAUST students whose research is relevant to the conference theme ‘Computational Advances in Structural Biology’. To register, please click on the register button below. Once registered, you will receive instructions and submission codes for uploading your poster electronically. Register The deadline for submissions is April 1, 2023. The poster competition will be judged by an external panel of experts, and prizes will be awarded

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of
<> big data (5)

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Exploring science's fourth paradigm

1 min read ·
The recent KAUST Research Conference: Computational and Statistical Interface to Big Data brought together leading computer science researchers and statistical experts to discuss the current state and future of data science. Held on campus from March 19 to 21, the conference covered such data science topics as succinct data representation and storage; big data visualization; parallel and distributed algorithms for inference and optimization; and analysis of large graphs and networks.

CEMSE Division startups in focus: Peregrine Genomics

1 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.
<> data mining (5)

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a
<> systems biology (5)

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Meshari Alazmi

As a Ph.D. student in Xin Gao's group, Meshari earned his degree in synthetic biology, systems biology, thermodynamics, and protein structure prediction. Thereafter he worked in a similar capacity in Stefan Arold's group and later went on to become an assistant professor at the University of Hail, KSA. Research Interests Meshari Alazmi's research interests are in ​Synthetic Biology, Systems Biology, Thermodynamics, Protein Structure Prediction Scientific and Professional Membership Association for Computing Machinery (ACM) student Chapter Education Profile Ph.D. in computer science, King
<> algorithm (4) <> healthcare (4) <> human health (4) <> Medical imaging (4)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Meet KAUST prospective student: Juexiao Zhou

1 min read ·
Juexiao Zhou is a 21-year-old graduate from Shenzhen, China, who will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate and member of the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

Juexiao Zhou

Ph.D. Student, Computer Science
Juexiao Zhou is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Juexiao majored in Bioinformatics and graduated from the Southern University of Science and Technology (SUSTech), located in Shenzhen, Guangdong, China. Research Interests IngIntelligent Healthcare: develop machine learning (ML) / deep learning (DL) techniques for healthcare, including disease detection, risk assessment, etc. Bioinformatics: develop new methods for bioinformatics tasks, including gene regulation understanding

Islam Almasri

Islam Almasri obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Islam is a Ph.D. candidate in the Computer Science program under the supervision of Prof. Nina Fedoroff. at Fedoroff Research Group. Islam is specialized in developing various software tools for solving problems in other scientific domains, including Image Processing, Medical Imaging, Chemical Networks, and Protein Structure. Selected Publications Almasri, Islam
<> Reinforcement Learning (4) <> Signal processing (4) <> synthetic biology (4)

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

Meshari Alazmi

As a Ph.D. student in Xin Gao's group, Meshari earned his degree in synthetic biology, systems biology, thermodynamics, and protein structure prediction. Thereafter he worked in a similar capacity in Stefan Arold's group and later went on to become an assistant professor at the University of Hail, KSA. Research Interests Meshari Alazmi's research interests are in ​Synthetic Biology, Systems Biology, Thermodynamics, Protein Structure Prediction Scientific and Professional Membership Association for Computing Machinery (ACM) student Chapter Education Profile Ph.D. in computer science, King

Majed Alzahrani

Majed Alzahrani obtained PhD degree in Computer Science from King Abdullah University of Science and Technology in 2017. His PhD thesis supervisor was Professor Xin Gao. After graduating, he joined Saudi Aramco as a data scientist in its Drilling Data Science unit. Working in one of the largest companies in the world is a dream come true for many, for Majed Alzahrani, things couldn't be better. Research Interest Majed's research interests were in machine learning, structural bioinformatics, synthetic biology.

Hiroyuki Kuwahara

Hiroyuki Kuwahara got his Ph.D. degree in Computer Science from the University of Utah in 2008. He was then a junior researcher at Microsoft Research - Trento during 2007 and 2009. During 2009 and 2012, he was a Ray and Stephanie Lane Fellow at the School of Computer Science at Carnegie Mellon University. Since 2012, he joined the SFB group as a Research Scientist. Research Interests ​Hiroyuki's area of research is broadly in computational systems and synthetic biology. His main research focus is on the theoretical understanding of how various uncertainties contribute to the relation between
<> AI (3) <> bioscience (3) <> cancer (3)

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

Christophe Van Neste

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Christophe Van Neste is a Postdoctoral Fellow under the supervision of Professor Xin Gao at the Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests ​My topic interests are centered around cancer and development, specifically related to the embryonal cancer neuroblastoma and how it links to the normal development of the sympathetic neuronal system. My focus there lies on replicative stress and secondary DNA structures such as G-quadruplexes and how that impacts development or cancer stem cell maintenance. My
<> data analysis (3)

Nizar Ajeeb

Full Time Researcher Full at King Abdullah University of Science and Technology.

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main
<> data integration (3)

Mona Alshahrani

Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network

Alyaa M Mohamed

Ph.D., Bioscience
Alyaa M Mohamed obtained her Ph.D. degree under the co-supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group and Prof. Takashi Gojobori at King Abdullah University of Science and Technology (KAUST). Currently, Alyaa is a Postdoc Fellow at University of Calgary, Canada. Research Interests Genome-scale metabolic modeling, Gene expression analysis, Constraint-based modeling. Education Profile Ph.D. in Bioscience, King Abdullah University of Science and Technology, Saudi Arabia, 2012-2018. M.Sc. in Pharmacology, German University in Cairo, Egypt, 2010. ​B.Sc. in Pharmacy

Adil Salhi

Adil Salhi is a Research Engineer at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Adil obtained his Ph.D. degree in Computer Science from the University of Manchester, UK. Research Interests Adil's research interests lie in the area of data and text mining of biomedical literature. The extraction of new information from disparate structured data sources, or available information buried in free-form text, requires the deployment of a wide array of tools to overcome the challenges posed by the nature and size
<> genome (3) <> haps (3)

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022
H

Hussein Al-Shatri

I chose KAUST because of its reputation for excellence in research and its recent emphasis on applied research. With my extensive background in both academic research and industrial development, I am excited to contribute my experience and ambition. My goal is to foster new ideas and develop innovative prototypes in the field of wireless communications, which will undoubtedly enhance people's lives.
<> IoT (3) <> MATLAB (3) <> protein structures (3)

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Islam Almasri

Islam Almasri obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Islam is a Ph.D. candidate in the Computer Science program under the supervision of Prof. Nina Fedoroff. at Fedoroff Research Group. Islam is specialized in developing various software tools for solving problems in other scientific domains, including Image Processing, Medical Imaging, Chemical Networks, and Protein Structure. Selected Publications Almasri, Islam

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc
<> RNA genes (3)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Computational Analysis of Transcriptional Regulation after Single and Multiple Drug Administration.

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B3 L5 R5209

With the advances in transcriptomic analysis, the monitoring of genome-wide gene expression provides a powerful approach for determining the action of drugs. In this thesis, we analyzed the transcriptional responses of cells treated with drugs either alone or in combinations to explore their effects in two different applications: breast cancer therapy and cell conversion.

Novel computational methods for promoter identification and analysis

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B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> AI4Science (2) <> applied mathematics (2) <> comparative genomics (2) <> computational methods (2) <> COVID-19 (2) <> Drug Combinations (2) <> drug effects (2) <> Generative AI and LLMs (2) <> genomics (2) <> image processing (2) <> ISAC (2) <> Localization (2) <> modeling (2) <> Natural Language Processing (2) <> Network Design (2) <> neural network (2) <> Non-Terrestrial Networks (2) <> phenotypes (2) <> prediction tools (2)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> Programming language (2) <> promoters (2)

Promoters identification and analysis

-

B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> protein coding (2)

Promoters identification and analysis

-

B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> protein structure prediction (2) <> quantum communication (2) <> text mining (2)

Christophe Van Neste

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Christophe Van Neste is a Postdoctoral Fellow under the supervision of Professor Xin Gao at the Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests ​My topic interests are centered around cancer and development, specifically related to the embryonal cancer neuroblastoma and how it links to the normal development of the sympathetic neuronal system. My focus there lies on replicative stress and secondary DNA structures such as G-quadruplexes and how that impacts development or cancer stem cell maintenance. My

Adil Salhi

Adil Salhi is a Research Engineer at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Adil obtained his Ph.D. degree in Computer Science from the University of Manchester, UK. Research Interests Adil's research interests lie in the area of data and text mining of biomedical literature. The extraction of new information from disparate structured data sources, or available information buried in free-form text, requires the deployment of a wide array of tools to overcome the challenges posed by the nature and size
<> TSS (2)

Promoters identification and analysis

-

B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> 5G networks (1) <> 6g wireless systems (1) <> Action Recognition (1) <> Action understanding (1) <> adaptive control (1) <> Adaptive Optics (1) <> Aerial Robotics (1) <> AI for healthcare (1) <> AI-generated text (1) <> applied statistics (1) <> beamforming (1) <> biology (1) <> biomechanics (1) <> biomedical data (1) <> Biomedical Signal Processing (1) <> biotechnology (1) <> BLE communication (1) <> breast cancer (1) <> calculus of variations (1) <> cell conversion (1) <> Cellular Networks (1) <> Chaos Shift Keying (1) <> cloud- and fog-radio access networks (1) <> combustion (1) <> communication (1) <> communication networks (1) <> communication systems (1) <> Compressed Sensing Approach (1) <> computation (1) <> computational and statistical interface (1) <> Computational modeling (1) <> Computational Photography (1) <> computational science (1) <> computer graphics (1) <> computer networks (1) <> Continual Learning (1) <> control systems (1) <> Cooperative networks (1) <> cross-layer optimization (1) <> cwDTW algorithm (1) <> cybersecurity (1) <> Data Analyst (1) <> data science (1) <> Deep generative models (1) <> development (1)