About Siyuan Chen Siyuan Chen M.S. Student, Computer Science Computational biology machine learning 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. Articles Related News March 2023 Guidelines for Cellular Deconvolution in Spatial Transcriptomics 1 min read · Thu, Mar 30 2023 News A recent Nature paper titled "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics" by Haoyang Li, Juexiao Zhou, Zhongxiao Li, Siyuan Chen, Xingyu Liao, Bin Zhang, Ruochi Zhang, Yu Wang, Shiwei Sun, and Prof. Xin Gao evaluates 18 existing cellular deconvolution methods used in spatial transcriptomics using 50 real-world and simulated datasets. To measure accuracy, robustness, and usefulness, the authors compare the approaches using various metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. The July 2021 Training an AI eye on the Moon 1 min read · Mon, Jul 12 2021 News machine learning algorithm modeling Computer science Machine learning accelerates the search for promising Moon sites for energy and mineral resources. July 2020 KAUST Prospective Student: Siyuan Chen 2 min read · Thu, Jul 2 2020 News artificial intelligence IoT 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.
Guidelines for Cellular Deconvolution in Spatial Transcriptomics 1 min read · Thu, Mar 30 2023 News A recent Nature paper titled "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics" by Haoyang Li, Juexiao Zhou, Zhongxiao Li, Siyuan Chen, Xingyu Liao, Bin Zhang, Ruochi Zhang, Yu Wang, Shiwei Sun, and Prof. Xin Gao evaluates 18 existing cellular deconvolution methods used in spatial transcriptomics using 50 real-world and simulated datasets. To measure accuracy, robustness, and usefulness, the authors compare the approaches using various metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. The
Training an AI eye on the Moon 1 min read · Mon, Jul 12 2021 News machine learning algorithm modeling Computer science Machine learning accelerates the search for promising Moon sites for energy and mineral resources.
KAUST Prospective Student: Siyuan Chen 2 min read · Thu, Jul 2 2020 News artificial intelligence IoT 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.
