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| PrePPI: a structure-informed database of protein–protein interactions QC Zhang, D Petrey, JI Garzón, L Deng, B Honig Nucleic acids research 41 (D1), D828-D833, 2012 | 275 | 2012 |
| DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy H Liu, W Zhang, B Zou, J Wang, Y Deng, L Deng Nucleic acids research 48 (D1), D871-D881, 2020 | 149 | 2020 |
| Integrating multiple heterogeneous networks for novel lncRNA-disease association inference J Zhang, Z Zhang, Z Chen, L Deng IEEE/ACM transactions on computational biology and bioinformatics 16 (2 …, 2017 | 120 | 2017 |
| PredUs: a web server for predicting protein interfaces using structural neighbors QC Zhang, L Deng, M Fisher, J Guan, B Honig, D Petrey Nucleic acids research 39 (suppl_2), W283-W287, 2011 | 116 | 2011 |
| DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations J Wang, X Liu, S Shen, L Deng, H Liu Briefings in Bioinformatics 23 (1), bbab390, 2022 | 93 | 2022 |
| Enhanced prediction of hot spots at protein-protein interfaces using extreme gradient boosting H Wang, C Liu, L Deng Scientific reports 8 (1), 14285, 2018 | 88 | 2018 |
| Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks Y Xiao, J Zhang, L Deng Scientific reports 7 (1), 3664, 2017 | 87 | 2017 |
| Computational identification of binding energy hot spots in protein–RNA complexes using an ensemble approach Y Pan, Z Wang, W Zhan, L Deng Bioinformatics 34 (9), 1473-1480, 2018 | 85 | 2018 |
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| Machine learning approaches for protein–protein interaction hot spot prediction: Progress and comparative assessment S Liu, C Liu, L Deng Molecules 23 (10), 2535, 2018 | 78 | 2018 |
| KATZLGO: large-scale prediction of LncRNA functions by using the KATZ measure based on multiple networks Z Zhang, J Zhang, C Fan, Y Tang, L Deng IEEE/ACM transactions on computational biology and bioinformatics 16 (2 …, 2017 | 77 | 2017 |
| PredHS: a web server for predicting protein–protein interaction hot spots by using structural neighborhood properties L Deng, QC Zhang, Z Chen, Y Meng, J Guan, S Zhou Nucleic acids research 42 (W1), W290-W295, 2014 | 68 | 2014 |
| A computational interactome and functional annotation for the human proteome JI Garzon, L Deng, D Murray, S Shapira, D Petrey, B Honig Elife 5, e18715, 2016 | 64 | 2016 |
| Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification J Zhang, Z Zhang, Z Wang, Y Liu, L Deng Bioinformatics 34 (10), 1750-1757, 2018 | 57 | 2018 |
| Boosting prediction performance of protein–protein interaction hot spots by using structural neighborhood properties L Deng, J Guan, X Wei, Y Yi, QC Zhang, S Zhou Journal of Computational Biology 20 (11), 878-891, 2013 | 55 | 2013 |
| SDN2GO: an integrated deep learning model for protein function prediction Y Cai, J Wang, L Deng Frontiers in bioengineering and biotechnology 8, 391, 2020 | 52 | 2020 |
| Template-based prediction of protein function D Petrey, TS Chen, L Deng, JI Garzon, H Hwang, G Lasso, H Lee, ... Current opinion in structural biology 32, 33-38, 2015 | 48 | 2015 |
| PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility C Fan, D Liu, R Huang, Z Chen, L Deng Bmc Bioinformatics 17, 85-95, 2016 | 47 | 2016 |
| Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties Y Pan, D Liu, L Deng PloS one 12 (6), e0179314, 2017 | 46 | 2017 |
