| Applications of machine learning in drug discovery and development J Vamathevan, D Clark, P Czodrowski, I Dunham, E Ferran, G Lee, B Li, ... Nature reviews Drug discovery 18 (6), 463-477, 2019 | 2335 | 2019 |
| Image analysis and machine learning in digital pathology: Challenges and opportunities A Madabhushi, G Lee Medical image analysis 33, 170-175, 2016 | 989 | 2016 |
| Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data A Madabhushi, S Agner, A Basavanhally, S Doyle, G Lee Computerized medical imaging and graphics 35 (7-8), 506-514, 2011 | 166 | 2011 |
| Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies G Lee, C Rodriguez, A Madabhushi IEEE/ACM Transactions on Computational Biology and Bioinformatics 5 (3), 368-384, 2008 | 140 | 2008 |
| Supervised multi-view canonical correlation analysis (sMVCCA): Integrating histologic and proteomic features for predicting recurrent prostate cancer G Lee, A Singanamalli, H Wang, MD Feldman, SR Master, NNC Shih, ... IEEE transactions on medical imaging 34 (1), 284-297, 2014 | 114 | 2014 |
| Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients G Lee, R Sparks, S Ali, NNC Shih, MD Feldman, E Spangler, T Rebbeck, ... PloS one 9 (5), e97954, 2014 | 88 | 2014 |
| Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images P Leo, G Lee, NNC Shih, R Elliott, MD Feldman, A Madabhushi Journal of medical imaging 3 (4), 047502-047502, 2016 | 83 | 2016 |
| Nuclear shape and architecture in benign fields predict biochemical recurrence in prostate cancer patients following radical prostatectomy: preliminary findings G Lee, RW Veltri, G Zhu, S Ali, JI Epstein, A Madabhushi European urology focus 3 (4-5), 457-466, 2017 | 69 | 2017 |
| Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays G Lee, S Ali, R Veltri, JI Epstein, C Christudass, A Madabhushi Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013 | 59 | 2013 |
| Integrated diagnostics: a conceptual framework with examples A Madabhushi, S Doyle, G Lee, A Basavanhally, J Monaco, S Masters, ... Clinical chemistry and laboratory medicine 48 (7), 989-998, 2010 | 50 | 2010 |
| Feature importance in nonlinear embeddings (FINE): applications in digital pathology SB Ginsburg, G Lee, S Ali, A Madabhushi IEEE transactions on medical imaging 35 (1), 76-88, 2015 | 45 | 2015 |
| A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: Preliminary results in predicting prostate cancer recurrence … G Lee, S Doyle, J Monaco, A Madabhushi, MD Feldman, SR Master, ... 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2009 | 43 | 2009 |
| Evaluating feature selection strategies for high dimensional, small sample size datasets A Golugula, G Lee, A Madabhushi 2011 Annual International conference of the IEEE engineering in medicine and …, 2011 | 35 | 2011 |
| Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data P Tiwari, S Viswanath, G Lee, A Madabhushi 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2011 | 35 | 2011 |
| Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab±ipilimumab V Baxi, G Lee, C Duan, D Pandya, DN Cohen, R Edwards, H Chang, J Li, ... Modern Pathology 35 (11), 1529-1539, 2022 | 26 | 2022 |
| Advances in the computational and molecular understanding of the prostate cancer cell nucleus NM Carleton, G Lee, A Madabhushi, RW Veltri Journal of cellular biochemistry 119 (9), 7127-7142, 2018 | 24 | 2018 |
| Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases SE Viswanath, P Tiwari, G Lee, A Madabhushi, ... BMC medical imaging 17, 1-17, 2017 | 22 | 2017 |
| An empirical comparison of dimensionality reduction methods for classifying gene and protein expression datasets G Lee, C Rodriguez, A Madabhushi Bioinformatics Research and Applications: Third International Symposium …, 2007 | 22 | 2007 |
| Computer extracted features from initial H&E tissue biopsies predict disease progression for prostate cancer patients on active surveillance S Chandramouli, P Leo, G Lee, R Elliott, C Davis, G Zhu, P Fu, JI Epstein, ... Cancers 12 (9), 2708, 2020 | 20 | 2020 |
| Supervised multi-view canonical correlation analysis: Fused multimodal prediction of disease diagnosis and prognosis A Singanamalli, H Wang, G Lee, N Shih, M Rosen, S Master, ... Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and …, 2014 | 14 | 2014 |