Template-based protein structure modeling using the RaptorX web server M Källberg, H Wang, S Wang, J Peng, Z Wang, H Lu, J Xu Nature protocols 7 (8), 1511-1522, 2012 | 1889 | 2012 |
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model S Wang, S Sun, Z Li, R Zhang, J Xu PLOS Computational Biology 13 (1), e1005324, 2017 | 991 | 2017 |
Protein secondary structure prediction using deep convolutional neural fields S Wang, J Peng, J Ma, J Xu Scientific reports 6, 18962, 2016 | 669 | 2016 |
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling X Yan, C Zheng, Z Li, S Wang, S Cui Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 606 | 2020 |
RaptorX-Property: a web server for protein structure property prediction S Wang, W Li, S Liu, J Xu Nucleic acids research 44 (W1), W430-W435, 2016 | 561 | 2016 |
RaptorX server: a resource for template-based protein structure modeling M Källberg, G Margaryan, S Wang, J Ma, J Xu Protein Structure Prediction, 17-27, 2014 | 301 | 2014 |
DEEPre: sequence-based enzyme EC number prediction by deep learning Y Li, S Wang, R Umarov, B Xie, M Fan, L Li, X Gao Bioinformatics, btx680, 2017 | 273 | 2017 |
Critical assessment of protein intrinsic disorder prediction M Necci, D Piovesan, SCE Tosatto Nature methods 18 (5), 472-481, 2021 | 219 | 2021 |
Protein structure alignment beyond spatial proximity S Wang, J Ma, J Peng, J Xu Scientific reports 3, 1448, 2013 | 209 | 2013 |
Protein threading using context-specific alignment potential J Ma, S Wang, F Zhao, J Xu Bioinformatics 29 (13), i257-i265, 2013 | 175 | 2013 |
Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning H Teng, MD Cao, MB Hall, T Duarte, S Wang, LJM Coin GigaScience 7 (5), giy037, 2018 | 169 | 2018 |
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning J Ma, S Wang, Z Wang, J Xu Bioinformatics, btv472, 2015 | 138 | 2015 |
ComplexContact: a web server for inter-protein contact prediction using deep learning H Zeng, S Wang, T Zhou, F Zhao, X Li, Q Wu, J Xu Nucleic acids research 46 (W1), W432-W437, 2018 | 137 | 2018 |
Analysis of distance‐based protein structure prediction by deep learning in CASP13 J Xu, S Wang Proteins: Structure, Function, and Bioinformatics 87 (12), 1069-1081, 2019 | 133 | 2019 |
AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields S Wang, J Ma, J Xu Bioinformatics 32 (17), i672-i679, 2016 | 130 | 2016 |
DeepSimulator: a deep simulator for Nanopore sequencing Y Li, R Han, C Bi, M Li, S Wang, X Gao Bioinformatics 34 (17), 2899-2908, 2018 | 107 | 2018 |
Analysis of deep learning methods for blind protein contact prediction in CASP12 S Wang, S Sun, J Xu Proteins: Structure, Function, and Bioinformatics 86 (S1), 67-77, 2018 | 102 | 2018 |
A conditional neural fields model for protein threading J Ma, J Peng, S Wang, J Xu Bioinformatics 28 (12), i59-i66, 2012 | 100 | 2012 |
Instancerefer: Cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring Z Yuan, X Yan, Y Liao, R Zhang, S Wang, Z Li, S Cui Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 98 | 2021 |
Shallow feature matters for weakly supervised object localization J Wei, Q Wang, Z Li, S Wang, SK Zhou, S Cui Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 95 | 2021 |