DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning W Li, WH Wong, R Jiang Nucleic acids research 47 (10), e60-e60, 2019 | 107 | 2019 |
A method for scoring the cell type-specific impacts of noncoding variants in personal genomes W Li, Z Duren, R Jiang, WH Wong Proceedings of the National Academy of Sciences 117 (35), 21364-21372, 2020 | 14 | 2020 |
Gene co-opening network deciphers gene functional relationships W Li, M Wang, J Sun, Y Wang, R Jiang Molecular BioSystems 13 (11), 2428-2439, 2017 | 10 | 2017 |
Mimvec: a deep learning approach for analyzing the human phenome M Gan, W Li, W Zeng, X Wang, R Jiang BMC systems biology 11, 3-16, 2017 | 8 | 2017 |
Associating divergent lncRNAs with target genes by integrating genome sequence, gene expression and chromatin accessibility data Y Wang, S Chen, W Li, R Jiang, Y Wang NAR Genomics and Bioinformatics 2 (2), lqaa019, 2020 | 5 | 2020 |
EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model M Gan, W Li, R Jiang PeerJ 7, e7657, 2019 | 4 | 2019 |
Validation of three European risk scores to predict long-term outcomes for patients receiving cardiac resynchronization therapy in an Asian population S Yang, Z Liu, W Li, Y Hu, S Liu, R Jing, W Hua Journal of Cardiovascular Translational Research 14, 754-760, 2021 | 2 | 2021 |
DeepTACT: predicting high-resolution chromatin contacts via bootstrapping deep learning W Li, WH Wong, R Jiang bioRxiv, 353284, 2018 | 2 | 2018 |