Author

Sheng Yu

Tsinghua UniversityORCID 0000-0002-6347-0507Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Sheng Yu1 paper · Agricultural Genomics Institute at Shenzhen

Biology Disciplines

Click to filter author papers

Bioinformatics· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Assay:electronic health records· 1 Organism:human· 1

Frequent Co-Authors

Christopher J. O’DonnelliD· 1 shared Chuan HongiD· 1 shared Chuanyang Qin· 1 shared Di XiongiD· 1 shared Hongtu Zhu BiD· 1 shared Huiyuan CheniD· 1 shared Isaac S. KohaneiD· 1 shared J. Michael GazianoiD· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperBioinformatics

Christopher J. O’DonnellORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Alzheimer Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

Yue Yang, Kaixian Yu, Shan Gao, Sheng Yu, Di Xiong, Chuanyang Qin, Huiyuan Chen, Jiarui Tang, Niansheng Tang, Hongtu Zhu B

The paper constructs an Alzheimer's Disease Knowledge Graph (ADKG) to enhance disease prediction and knowledge discovery, showing improved performance in predictive modeling.

Computers in Biology and Medicine · Apr 29, 2025Peer-reviewedBioinformatics

High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS

Katherine P. Liao, Jiehuan Sun, Tianrun A. Cai, Nicholas Link, Chuan Hong, Jie Huang, Jennifer E. Huffman, Jessica L. Gronsbell, Yichi Zhang, Yuk-Lam Anne Ho, Victor Manuel Castro, Vivian S. Gainer, Shawn N. Murphy, Christopher J. O’Donnell, J. Michael Gaziano, , with the VA Million Veteran Program

This paper develops an automated high-throughput phenotyping method (MAP) that improves phenotype accuracy and scalability for PheWAS studies.

Journal of the American Medical Informatics Association · Apr 26, 2019Peer-reviewedBioinformatics