| Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes X Zhou, P Feliciano, C Shu, T Wang, I Astrovskaya, JB Hall, JU Obiajulu, ... Nature genetics 54 (9), 1305-1319, 2022 | 257 | 2022 |
| Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes P Feliciano, X Zhou, I Astrovskaya, TN Turner, T Wang, L Brueggeman, ... NPJ genomic medicine 4 (1), 19, 2019 | 226 | 2019 |
| MVP predicts the pathogenicity of missense variants by deep learning H Qi, H Zhang, Y Zhao, C Chen, JJ Long, WK Chung, Y Guan, Y Shen Nature communications 12 (1), 510, 2021 | 179* | 2021 |
| Predicting functional effect of missense variants using graph attention neural networks H Zhang, MS Xu, X Fan, WK Chung, Y Shen Nature Machine Intelligence 4 (11), 1017-1028, 2022 | 52 | 2022 |
| Improving protein fold recognition by extracting fold-specific features from predicted residue–residue contacts J Zhu, H Zhang, SC Li, C Wang, L Kong, S Sun, WM Zheng, D Bu Bioinformatics 33 (23), 3749-3757, 2017 | 47 | 2017 |
| FALCON@ home: a high-throughput protein structure prediction server based on remote homologue recognition C Wang, H Zhang, WM Zheng, D Xu, J Zhu, B Wang, K Ning, S Sun, SC Li, ... Bioinformatics 32 (3), 462-464, 2016 | 46 | 2016 |
| Protein structure prediction: challenges, advances, and the shift of research paradigms B Huang, L Kong, C Wang, F Ju, Q Zhang, J Zhu, T Gong, H Zhang, C Yu, ... Genomics, proteomics & bioinformatics 21 (5), 913-925, 2023 | 37 | 2023 |
| Accurate and efficient protein sequence design through learning concise local environment of residues B Huang, T Fan, K Wang, H Zhang, C Yu, S Nie, Y Qi, WM Zheng, J Han, ... Bioinformatics 39 (3), btad122, 2023 | 31 | 2023 |
| Template-based prediction of protein structure with deep learning H Zhang, Y Shen BMC genomics 21, 1-9, 2020 | 31 | 2020 |
| Insights into the transmission of respiratory infectious diseases through empirical human contact networks C Huang, X Liu, S Sun, SC Li, M Deng, G He, H Zhang, C Wang, Y Zhou, ... Scientific reports 6 (1), 31484, 2016 | 27 | 2016 |
| Improving residue–residue contact prediction via low-rank and sparse decomposition of residue correlation matrix H Zhang, Y Gao, M Deng, C Wang, J Zhu, SC Li, WM Zheng, D Bu Biochemical and biophysical research communications 472 (1), 217-222, 2016 | 25 | 2016 |
| Improving prediction of burial state of residues by exploiting correlation among residues H Gong, H Zhang, J Zhu, C Wang, S Sun, WM Zheng, D Bu BMC bioinformatics 18, 165-175, 2017 | 17 | 2017 |
| Accurate and robust protein sequence design with CarbonDesign M Ren, C Yu, D Bu, H Zhang Nature Machine Intelligence 6 (5), 536-547, 2024 | 15* | 2024 |
| Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model S Liu, T Zhu, M Ren, C Yu, D Bu, H Zhang NeurIPS 2023, 2023 | 15 | 2023 |
| Predicting protein inter-residue contacts using composite likelihood maximization and deep learning H Zhang, Q Zhang, F Ju, J Zhu, Y Gao, Z Xie, M Deng, S Sun, WM Zheng, ... BMC bioinformatics 20, 1-11, 2019 | 15 | 2019 |
| Microblog-HAN: A micro-blog rumor detection model based on heterogeneous graph attention network B Bi, Y Wang, H Zhang, Y Gao Plos one 17 (4), e0266598, 2022 | 10 | 2022 |
| CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model M Ren, T Zhu, H Zhang Forty-first International Conference on Machine Learning, 2024 | 6 | 2024 |
| Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints T Zhu, M Ren, H Zhang Forty-first International Conference on Machine Learning, 2024 | 5 | 2024 |
| FALCON2: a web server for high-quality prediction of protein tertiary structures L Kong, F Ju, H Zhang, S Sun, D Bu BMC bioinformatics 22, 1-14, 2021 | 4 | 2021 |
| A survey on algorithms for protein contact prediction H Zhang, Y Gao, M Deng, W Zheng, D Bu J Comput Res Dev 54 (1), 1-19, 2017 | 4 | 2017 |
