| Classification and regression by randomForest A Liaw, M Wiener R news 2 (3), 18-22, 2002 | 27991 | 2002 |
| Random forest: a classification and regression tool for compound classification and QSAR modeling V Svetnik, A Liaw, C Tong, JC Culberson, RP Sheridan, BP Feuston Journal of chemical information and computer sciences 43 (6), 1947-1958, 2003 | 3948 | 2003 |
| gplots: Various R programming tools for plotting data GR Warnes, B Bolker, L Bonebakker, R Gentleman, W Huber, A Liaw, ... R package version 2 (4), 1, 2009 | 3245 | 2009 |
| Newer classification and regression tree techniques: bagging and random forests for ecological prediction AM Prasad, LR Iverson, A Liaw Ecosystems 9, 181-199, 2006 | 2662 | 2006 |
| Using random forest to learn imbalanced data C Chen, A Liaw, L Breiman University of California, Berkeley 110 (1-12), 24, 2004 | 2047 | 2004 |
| Deep neural nets as a method for quantitative structure–activity relationships J Ma, RP Sheridan, A Liaw, GE Dahl, V Svetnik Journal of chemical information and modeling 55 (2), 263-274, 2015 | 1397 | 2015 |
| Extreme gradient boosting as a method for quantitative structure–activity relationships RP Sheridan, WM Wang, A Liaw, J Ma, EM Gifford Journal of chemical information and modeling 56 (12), 2353-2360, 2016 | 496 | 2016 |
| Improved statistical methods for hit selection in high-throughput screening C Brideau, B Gunter, B Pikounis, A Liaw SLAS Discovery 8 (6), 634-647, 2003 | 447 | 2003 |
| Application of Breiman’s random forest to modeling structure-activity relationships of pharmaceutical molecules V Svetnik, A Liaw, C Tong, T Wang International workshop on multiple Classifier systems, 334-343, 2004 | 381 | 2004 |
| Package ‘randomforest’ A Liaw, M Wiener, L Breiman, A Cutler University of California, Berkeley: Berkeley, CA, USA, 2018 | 275 | 2018 |
| Boosting: An ensemble learning tool for compound classification and QSAR modeling V Svetnik, T Wang, C Tong, A Liaw, RP Sheridan, Q Song Journal of chemical information and modeling 45 (3), 786-799, 2005 | 260 | 2005 |
| Wiener M A Liaw Classification and regression by randomForest. R News 2 (3), 18-22, 2002 | 257 | 2002 |
| Demystifying multitask deep neural networks for quantitative structure–activity relationships Y Xu, J Ma, A Liaw, RP Sheridan, V Svetnik Journal of chemical information and modeling 57 (10), 2490-2504, 2017 | 238 | 2017 |
| Package ‘gplots’ MGR Warnes, B Bolker, L Bonebakker, R Gentleman, W Huber, A Liaw Various R programming tools for plotting data, 112-119, 2016 | 230 | 2016 |
| gplots: Various R programming tools for plotting data. R package version 2.12. 1 GR Warnes, B Bolker, L Bonebakker, R Gentleman, W Huber, A Liaw, ... http://CRAN. R-project. org/package= gplots, last accessed March 17, 2017, 2013 | 223 | 2013 |
| Deep dive into machine learning models for protein engineering Y Xu, D Verma, RP Sheridan, A Liaw, J Ma, NM Marshall, J McIntosh, ... Journal of chemical information and modeling 60 (6), 2773-2790, 2020 | 222 | 2020 |
| Breiman and Cutler’s random forests for classification and regression A Liaw, M Wiener R package version 4, 6-12, 2015 | 189 | 2015 |
| Package ‘randomforest’ L Breiman, A Cutler, A Liaw, M Wiener, MA Liaw University of California, Berkeley: Berkeley, CA, USA 81, 1-29, 2018 | 118 | 2018 |
| The randomforest package A Liaw, M Wiener R news 2 (3), 18-22, 2002 | 116 | 2002 |
| Quantitative analysis of intact apolipoproteins in human HDL by top-down differential mass spectrometry MT Mazur, HL Cardasis, DS Spellman, A Liaw, NA Yates, ... Proceedings of the National Academy of Sciences 107 (17), 7728-7733, 2010 | 101 | 2010 |
