A gradient-boosting approach for filtering de novo mutations in parent-offspring trios. - Yongzhuang Liu; Bingshan Li; Renjie Tan; Xiaolin Zhu 002; Yadong Wang 001 (Bioinform., 2014)

A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis. - Rui Chen 021; Qiang Wei; Xiaowei Zhan; Xue Zhong; James S. Sutcliffe; Nancy J. Cox; Edwin H. Cook Jr.; Chun Li; Wei Chen 074; Bingshan Li (Bioinform., 2015)

A Bayesian framework for de novo mutation calling in parents-offspring trios. - Qiang Wei; Xiaowei Zhan; Xue Zhong; Yongzhuang Liu; Yujun Han; Wei Chen 074; Bingshan Li (Bioinform., 2015)

Joint detection of copy number variations in parent-offspring trios. - Yongzhuang Liu; Jian Liu; Jianguo Lu; Jiajie Peng; Liran Juan; Xiaolin Zhu 002; Bingshan Li; Yadong Wang 001 (Bioinform., 2016)

RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data. - Xiaowei Zhan; Youna Hu; Bingshan Li; Gonçalo R. Abecasis; Dajiang J. Liu (Bioinform., 2016)

A computational method for genotype calling in family-based sequencing data. - Lun-Ching Chang; Bingshan Li; Zhou Fang; Scott Vrieze; Matthew McGue; William G. Iacono; George C. Tseng; Wei Chen 074 (BMC Bioinform., 2016)

Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework. - Hai Yang; Qiang Wei; Xue Zhong; Hushan Yang; Bingshan Li (Bioinform., 2017)

De novo pattern discovery enables robust assessment of functional consequences of non-coding variants. - Hai Yang 002; Rui Chen 021; Quan Wang 004; Qiang Wei; Ying Ji 002; Guangze Zheng 002; Xue Zhong; Nancy J. Cox; Bingshan Li (Bioinform., 2019)

Ultrasound Image-Based Diagnosis of Cirrhosis with an End-to-End Deep Learning model. - Hai Yang; Xiaohui Sun; Yang Sun; Ligang Cui; Bingshan Li (BIBM, 2020)

DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data. - Yi Jiang; Gina Giase; Kay Grennan; Annie W. Shieh; Yan Xia; Lide Han; Quan Wang 004; Qiang Wei; Rui Chen 021; Sihan Liu; Kevin P. White; Chao Chen 041; et al. (PLoS Comput. Biol., 2020)

DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions. - Patrick Wu; Scott D. Nelson; Juan Zhao 003; Cosby A. Stone Jr.; QiPing Feng; Qingxia Chen; Eric A. Larson; Bingshan Li; Nancy J. Cox; C. Michael Stein; Elizabeth Phillips; Dan M. Roden; et al. (J. Am. Medical Informatics Assoc., 2021)

A computational framework to unify orthogonal information in DNA methylation and copy number aberrations in cell-free DNA for early cancer detection. - Qiang Wei; Chao Jin; Yang Wang; Shanshan Guo; Xu Guo; Xiaonan Liu; Jiaze An; Jinliang Xing; Bingshan Li (Briefings Bioinform., 2022)

A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization. - Ying Ji 002; Rui Chen 021; Quan Wang 004; Qiang Wei; Ran Tao; Bingshan Li (BMC Bioinform., 2022)

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning. - Hai Yang 002; Rui Chen 021; Quan Wang 004; Qiang Wei; Ying Ji 002; Xue Zhong; Bingshan Li (Bioinform., 2022)

FAVOR: functional annotation of variants online resource and annotator for variation across the human genome. - Hufeng Zhou; Theodore Arapoglou; Xihao Li; Zilin Li; Xiuwen Zheng 001; Jill Moore; Abhijith Asok; Sushant Kumar; Elizabeth E. Blue; Steven Buyske; Nancy J. Cox; Adam Felsenfeld; et al. (Nucleic Acids Res., 2023)

Improving Genetic Perturbation Response Prediction with an Enhanced Biological Knowledge Graph. - Yang Li; Rui Chen 021; Yuting Tan 005; Xue Zhong; Bingshan Li; Zhijun Yin (BIBM, 2024)

Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation. - Chao Yan 004; Monika E. Grabowska; Alyson L. Dickson; Bingshan Li; Zhexing Wen; Dan M. Roden; C. Michael Stein; Peter J. Embí; Josh F. Peterson; QiPing Feng; Bradley A. Malin; Wei-Qi Wei (npj Digit. Medicine, 2024)