| Predicting protein structural class with AdaBoost learner B Niu, YD Cai, WC Lu, GZ Li, KC Chou Protein and peptide letters 13 (5), 489-492, 2006 | 133 | 2006 |
| Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data R Liang, Y Lu, X Qu, Q Su, C Li, S Xia, Y Liu, Q Zhang, X Cao, Q Chen, ... Transboundary and emerging diseases 67 (2), 935-946, 2020 | 95 | 2020 |
| Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins B Niu, YH Jin, KY Feng, WC Lu, YD Cai, GZ Li Molecular diversity 12, 41-45, 2008 | 84 | 2008 |
| Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database X Zhou, L Xu, J Zhang, B Niu, M Luo, G Zhou, X Zhang Energy and Buildings 211, 109795, 2020 | 82 | 2020 |
| Prediction of protein–protein interactions based on PseAA composition and hybrid feature selection L Liu, Y Cai, W Lu, K Feng, C Peng, B Niu Biochemical and biophysical research communications 380 (2), 318-322, 2009 | 79 | 2009 |
| Predicting subcellular localization with AdaBoost Learner Y Jin, B Niu, KY Feng, WC Lu, YD Cai, GZ Li Protein and Peptide Letters 15 (3), 286-289, 2008 | 64 | 2008 |
| Application of machine learning approaches for protein-protein interactions prediction M Zhang, Q Su, Y Lu, M Zhao, B Niu Medicinal Chemistry 13 (6), 506-514, 2017 | 60 | 2017 |
| Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression Q Su, W Lu, D Du, F Chen, B Niu, KC Chou Oncotarget 8 (30), 49359, 2017 | 59 | 2017 |
| An epidemic avian influenza prediction model based on google trends Y Lu, S Wang, J Wang, G Zhou, Q Zhang, X Zhou, B Niu, Q Chen, ... Letters in Organic Chemistry 16 (4), 303-310, 2019 | 57 | 2019 |
| Role of miR-221/222 in tumor development and the underlying mechanism Q Song, Q An, B Niu, X Lu, N Zhang, X Cao Journal of Oncology 2019, 2019 | 56 | 2019 |
| Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores T Zhang, M Jiang, L Chen, B Niu, Y Cai BioMed research international 2013, 2013 | 48 | 2013 |
| Prediction of compounds’ biological function (metabolic pathways) based on functional group composition YD Cai, Z Qian, L Lu, KY Feng, X Meng, B Niu, GD Zhao, WC Lu Molecular diversity 12, 131-137, 2008 | 48 | 2008 |
| Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks B Niu, C Liang, Y Lu, M Zhao, Q Chen, Y Zhang, L Zheng, KC Chou Genomics 112 (1), 837-847, 2020 | 47 | 2020 |
| 2D-QSAR and 3D-QSAR analyses for EGFR inhibitors M Zhao, L Wang, L Zheng, M Zhang, C Qiu, Y Zhang, D Du, B Niu BioMed research international 2017, 2017 | 45 | 2017 |
| Progress in target drug molecules for Alzheimer's disease J Xie, R Liang, Y Wang, J Huang, X Cao, B Niu Current Topics in Medicinal Chemistry 20 (1), 4-36, 2020 | 41 | 2020 |
| Predicting toxic action mechanisms of phenols using AdaBoost Learner B Niu, Y Jin, WC Lu, GZ Li Chemometrics and Intelligent Laboratory Systems 96 (1), 43-48, 2009 | 40 | 2009 |
| Support vector machine for SAR/QSAR of phenethyl‐amines1 B Niu, W LU, S YANG, Y CAI, G LI Acta pharmacologica sinica 28 (7), 1075-1086, 2007 | 40 | 2007 |
| Identifying cancer targets based on machine learning methods via Chou’s 5-steps rule and general pseudo components R Liang, J Xie, C Zhang, M Zhang, H Huang, H Huo, X Cao, B Niu Current topics in medicinal chemistry 19 (25), 2301-2317, 2019 | 36 | 2019 |
| Different thermal processing effects on peanut allergenicity T Zhang, Y Shi, Y Zhao, J Wang, M Wang, B Niu, Q Chen Journal of the Science of Food and Agriculture 99 (5), 2321-2328, 2019 | 34 | 2019 |
| Application of machine learning approaches for the design and study of anticancer drugs Y Hu, Y Lu, S Wang, M Zhang, X Qu, B Niu Current Drug Targets 20 (5), 488-500, 2019 | 33 | 2019 |
