S
Siyuan Chen
Researcher at King Abdullah University of Science and Technology
Publications - 10
Citations - 55
Siyuan Chen is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Biology & Computer science. The author has an hindex of 2, co-authored 4 publications receiving 5 citations.
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Protein-RNA interaction prediction with deep learning: Structure matters
TL;DR: A thorough review of the protein-RNA interaction prediction field can be found in this paper, which surveys both the binding site and binding preference prediction problems and covers the commonly used datasets, features, and models.
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A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics
Haoyang Li,Juexiao Zhou,Zhongxiao Li,Siyuan Chen,Xingyu Liao,Bin Zhang,Ruochi Zhang,Yu Wang,Shiwei Sun,Xin Gao +9 more
TL;DR: In this paper , the authors benchmarked 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods.
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Lunar features detection for energy discovery via deep learning
TL;DR: This research aims at developing the first deep learning method to identify multiple lunar features simultaneously for potential energy source discovery, based on the state-of-the-art deep learning model, High Resolution Net, which can efficiently extract semantic information and high-resolution spatial information from the input images, which ensures the performance for recognizing the lunar features.
Posted ContentDOI
Self-supervised contrastive learning for integrative single cell RNA-seq data analysis
Wenkai Han,Wenkai Han,Yuqi Cheng,Yuqi Cheng,Jiayang Chen,Huawen Zhong,Zhihang Hu,Siyuan Chen,Licheng Zong,Irwin King,Xin Gao,Yu Li +11 more
TL;DR: Wang et al. as mentioned in this paper presented a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and downstream analysis, which overcomes the heterogeneity of the experimental data with a specifically designed representation learning task.
Posted ContentDOI
PPML-Omics: a Privacy-Preserving federated Machine Learning method protects patients’ privacy in omic data
Juexiao Zhou,Siyuan Chen,Yulian Wu,Haoyang Li,Longxi Zhou,Yan Hu,Zihang Xiang,Zhongxiao Li,Ningning Chen,Wenkai Han,Di Wang,Xin Gao +11 more
TL;DR: The theoretical proof of the privacy-preserving capability of PPML-Omics is given, suggesting the first mathematically guaranteed model with robust and generalizable empirical performance in omic data analysis.