| Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations W Zheng, C Zhang, Y Li, R Pearce, EW Bell, Y Zhang Cell reports methods 1 (3), 2021 | 460 | 2021 |
| I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction X Zhou, W Zheng, Y Li, R Pearce, C Zhang, EW Bell, G Zhang, Y Zhang Nature Protocols 17 (10), 2326-2353, 2022 | 272 | 2022 |
| Deeplearning contactmap guided protein structure prediction in CASP13 W Zheng, Y Li, C Zhang, R Pearce, SM Mortuza, Y Zhang Proteins: Structure, Function, and Bioinformatics 87 (12), 1149-1164, 2019 | 222 | 2019 |
| ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks Y Li, J Hu, C Zhang, DJ Yu, Y Zhang Bioinformatics 35 (22), 4647-4655, 2019 | 169 | 2019 |
| DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins C Zhang, W Zheng, SM Mortuza, Y Li, Y Zhang Bioinformatics 36 (7), 2105-2112, 2020 | 165 | 2020 |
| LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins W Zheng, C Zhang, Q Wuyun, R Pearce, Y Li, Y Zhang Nucleic acids research 47 (W1), W429-W436, 2019 | 152 | 2019 |
| Ensembling multiple raw coevolutionary features with deep residual neural networks for contactmap prediction in CASP13 Y Li, C Zhang, EW Bell, DJ Yu, Y Zhang Proteins: Structure, Function, and Bioinformatics, 2019 | 106 | 2019 |
| Predicting protein-DNA binding residues by weightedly combining sequence-based features and boosting multiple SVMs J Hu, Y Li, M Zhang, X Yang, HB Shen, DJ Yu IEEE/ACM transactions on computational biology and bioinformatics 14 (6 …, 2016 | 93 | 2016 |
| Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks Y Li, C Zhang, EW Bell, W Zheng, X Zhou, DJ Yu, Y Zhang PLoS computational biology 17 (3), e1008865, 2021 | 89 | 2021 |
| Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions SM Mortuza, W Zheng, C Zhang, Y Li, R Pearce, Y Zhang Nature communications 12 (1), 5011, 2021 | 74 | 2021 |
| ATPbind: accurate protein–ATP binding site prediction by combining sequence-profiling and structure-based comparisons J Hu, Y Li, Y Zhang, DJ Yu Journal of chemical information and modeling 58 (2), 501-510, 2018 | 60 | 2018 |
| Protein structure prediction using deep learning distance and hydrogenbonding restraints in CASP14 W Zheng, Y Li, C Zhang, X Zhou, R Pearce, EW Bell, X Huang, Y Zhang Proteins: Structure, Function, and Bioinformatics 89 (12), 1734-1751, 2021 | 59 | 2021 |
| FUpred: detecting protein domains through deep-learning-based contact map prediction W Zheng, X Zhou, Q Wuyun, R Pearce, Y Li, Y Zhang Bioinformatics 36 (12), 3749-3757, 2020 | 52 | 2020 |
| Detecting distant-homology protein structures by aligning deep neural-network based contact maps W Zheng, Q Wuyun, Y Li, SM Mortuza, C Zhang, R Pearce, J Ruan, ... PLoS computational biology 15 (10), e1007411, 2019 | 51 | 2019 |
| TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM J Hu, K Han, Y Li, JY Yang, HB Shen, DJ Yu Amino acids 48, 2533-2547, 2016 | 45 | 2016 |
| Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction Y Li, C Zhang, C Feng, R Pearce, P Lydia Freddolino, Y Zhang Nature Communications 14 (1), 5745, 2023 | 44 | 2023 |
| Progressive assembly of multi-domain protein structures from cryo-EM density maps X Zhou, Y Li, C Zhang, W Zheng, G Zhang, Y Zhang Nature computational science 2 (4), 265-275, 2022 | 37 | 2022 |
| Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data W Zheng, Q Wuyun, Y Li, C Zhang, PL Freddolino, Y Zhang Nature Methods 21 (2), 279-289, 2024 | 33 | 2024 |
| LOMETS3: Integrating deep learning and profile alignment for advanced protein template recognition and function annotation W Zheng, Q Wuyun, X Zhou, Y Li, PL Freddolino, Y Zhang Nucleic acids research 50 (W1), W454-W464, 2022 | 33 | 2022 |
| GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure J Hu, Y Li, JY Yang, HB Shen, DJ Yu Computational biology and chemistry 60, 59-71, 2016 | 31 | 2016 |
Yang ZhangNational University of Singapore在 nus.edu.sg 的电子邮件经过验证
