Author

Sheng Dai

Sun Yat-sen University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:GWAS / association mapping· 2 Organism:human· 2 Research Focus:variant effect prediction· 2 Assay:whole-exome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Miaoxin LiiD· 3 shared Pak Chung ShamiD· 3 shared Lin JiangiD· 2 shared Mulin Jun LiiD· 2 shared Binbin WangiD· 1 shared Bolan Yu· 1 shared Chao XueiD· 1 shared Clara Sze-Man TangiD· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersGenetics

Miaoxin LiORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases

Lin Jiang, Hui Jiang, Sheng Dai, Ying Chen, You‐Qiang Song, Clara Sze-Man Tang, Shirley Yin-Yu Pang, Shu‐Leong Ho, Binbin Wang, Maria-Mercedes Garcia-Barcelo, Paul Kwong Hang Tam, Stacey Shawn Cherny, Mulin Jun Li, Pak Chung Sham, Miaoxin Li

This paper introduces a new statistical method, RUNNER, to detect rare variants associated with complex diseases by comparing observed mutation burdens to predicted baseline values, showing improved power over existing methods.

Nucleic Acids Research · Dec 4, 2021Peer-reviewedGenetics

Estimating driver-tissues by robust selective expression of genes associated with complex diseases or traits

Lin Jiang, Chao Xue, Shangzhen Chen, Sheng Dai, Peikai Chen, Pak Chung Sham, Haijun Wang, Miaoxin Li

This paper introduces a framework to estimate driver tissues of complex diseases by analyzing selective gene expression from GWAS data, finding lung as a driver tissue for rheumatoid arthritis.

Genome biology · Nov 6, 2019Peer-reviewedGenetics

WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

Lin Jiang, Jingjing Zheng, Johnny Sheung Him Kwan, Sheng Dai, Cong Li, Mulin Jun Li, Bolan Yu, Ka Fai To, Pak Chung Sham, Yonghong Zhu, Miaoxin Li

WITER improves cancer-driver gene detection by accurately modeling background mutations, identifying 178 driver genes across 26 cancers with high validation rates.

bioRxiv · Feb 10, 2019 · PreprintBioinformatics