| 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 | 1958 | 2012 |
| A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information Y Luo, X Zhao, J Zhou, J Yang, Y Zhang, W Kuang, J Peng, L Chen, ... Nature communications 8 (1), 573, 2017 | 819 | 2017 |
| A community effort to assess and improve drug sensitivity prediction algorithms JC Costello, LM Heiser, E Georgii, M Gönen, MP Menden, NJ Wang, ... Nature biotechnology 32 (12), 1202-1212, 2014 | 817 | 2014 |
| Protein secondary structure prediction using deep convolutional neural fields S Wang, J Peng, J Ma, J Xu Scientific reports 6 (1), 18962, 2016 | 713 | 2016 |
| Widespread macromolecular interaction perturbations in human genetic disorders N Sahni, S Yi, M Taipale, JIF Bass, J Coulombe-Huntington, F Yang, ... Cell 161 (3), 647-660, 2015 | 611 | 2015 |
| A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach Y Cai, K Guan, J Peng, S Wang, C Seifert, B Wardlow, Z Li Remote sensing of environment 210, 35-47, 2018 | 536 | 2018 |
| Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches Y Cai, K Guan, D Lobell, AB Potgieter, S Wang, J Peng, T Xu, S Asseng, ... Agricultural and forest meteorology 274, 144-159, 2019 | 519 | 2019 |
| A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways M Taipale, G Tucker, J Peng, I Krykbaeva, ZY Lin, B Larsen, H Choi, ... Cell 158 (2), 434-448, 2014 | 473 | 2014 |
| High-resolution de novo structure prediction from primary sequence R Wu, F Ding, R Wang, R Shen, X Zhang, S Luo, C Su, Z Wu, Q Xie, ... BioRxiv, 2022.07. 21.500999, 2022 | 468 | 2022 |
| Empower sequence labeling with task-aware neural language model L Liu, J Shang, X Ren, F Xu, H Gui, J Peng, J Han Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 460 | 2018 |
| RaptorX: exploiting structure information for protein alignment by statistical inference J Peng, J Xu Proteins: Structure, Function, and Bioinformatics 79 (S10), 161-171, 2011 | 458 | 2011 |
| Computational solutions for omics data B Berger, J Peng, M Singh Nature reviews genetics 14 (5), 333-346, 2013 | 434 | 2013 |
| Variational Inference for Crowdsourcing Q Liu, J Peng, A Ihler Advances in Neural Information Processing Systems 25, 701-709, 2012 | 421 | 2012 |
| A community computational challenge to predict the activity of pairs of compounds M Bansal, et al Nature Biotechnology, 2014 | 343 | 2014 |
| Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ... Nature communications 10 (1), 2674, 2019 | 328 | 2019 |
| Selection bias at the heterosexual HIV-1 transmission bottleneck JM Carlson, M Schaefer, DC Monaco, R Batorsky, DT Claiborne, J Prince, ... Science 345 (6193), 1254031, 2014 | 298 | 2014 |
| Compact integration of multi-network topology for functional analysis of genes H Cho, B Berger, J Peng Cell systems 3 (6), 540-548. e5, 2016 | 268 | 2016 |
| A 3D generative model for structure-based drug design S Luo, J Guan, J Ma, J Peng Advances in Neural Information Processing Systems 34, 6229-6239, 2021 | 242 | 2021 |
| Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures S Luo, Y Su, X Peng, S Wang, J Peng, J Ma Advances in Neural Information Processing Systems 35, 9754-9767, 2022 | 235 | 2022 |
| Conditional neural fields J Peng, L Bo, J Xu NIPS 2009, 2009 | 229 | 2009 |
