Yufeng Shen
Affiliations: | Systems Biology | Columbia University Medical Center, New York |
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"Yufeng Shen"Parents
Sign in to add mentor| George M. Weinstock | grad student | Baylor College of Medicine (Computer Science Tree) |
Children
Sign in to add trainee| Guojie Zhong | grad student | 2019-2024 | Columbia University Medical Center, New York |
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Publications
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| Xu C, Bao S, Wang Y, et al. (2024) Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences. Genome Research |
| Watkins WS, Hernandez EJ, Miller TA, et al. (2024) Genome Sequencing is Critical for Forecasting Outcomes following Congenital Cardiac Surgery. Medrxiv : the Preprint Server For Health Sciences |
| Zhong G, Zhao Y, Zhuang D, et al. (2024) PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context. Biorxiv : the Preprint Server For Biology |
| Xiao F, Zhang X, Morton SU, et al. (2024) Functional dissection of human cardiac enhancers and noncoding de novo variants in congenital heart disease. Nature Genetics |
| Zhao Y, Zhong G, Hagen J, et al. (2023) A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data. Medrxiv : the Preprint Server For Health Sciences |
| Petit F, Longoni M, Wells J, et al. (2023) PLS3 missense variants affecting the actin-binding domains cause X-linked congenital diaphragmatic hernia and body-wall defects. American Journal of Human Genetics |
| Zhong G, Choi YA, Shen Y. (2023) VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants. Communications Biology. 6: 774 |
| Griffin EL, Nees SN, Morton SU, et al. (2023) Evidence-Based Assessment of Congenital Heart Disease Genes to Enable Returning Results in a Genomic Study. Circulation. Genomic and Precision Medicine. e003791 |
| Zhang H, Xu MS, Fan X, et al. (2022) Predicting functional effect of missense variants using graph attention neural networks. Nature Machine Intelligence. 4: 1017-1028 |
| Fan X, Pan H, Tian A, et al. (2022) SHINE: protein language model-based pathogenicity prediction for short inframe insertion and deletion variants. Briefings in Bioinformatics |