| Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles A Subramanian, P Tamayo, VK Mootha, S Mukherjee, BL Ebert, ... Proceedings of the National Academy of Sciences 102 (43), 15545-15550, 2005 | 37586 | 2005 |
| Choosing multiple parameters for support vector machines O Chapelle, V Vapnik, O Bousquet, S Mukherjee Machine learning 46, 131-159, 2002 | 3362 | 2002 |
| Prediction of central nervous system embryonal tumour outcome based on gene expression SL Pomeroy, P Tamayo, M Gaasenbeek, LM Sturla, M Angelo, ... Nature 415 (6870), 436-442, 2002 | 2808 | 2002 |
| Multiclass cancer diagnosis using tumor gene expression signatures S Ramaswamy, P Tamayo, R Rifkin, S Mukherjee, CH Yeang, M Angelo, ... Proceedings of the National Academy of Sciences 98 (26), 15149-15154, 2001 | 2515 | 2001 |
| Feature selection for SVMs J Weston, S Mukherjee, O Chapelle, M Pontil, T Poggio, V Vapnik Advances in neural information processing systems 13, 2000 | 1517 | 2000 |
| Nonlinear prediction of chaotic time series using support vector machines S Mukherjee, E Osuna, F Girosi Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE …, 1997 | 869 | 1997 |
| A genomic strategy to refine prognosis in early-stage non–small-cell lung cancer A Potti, S Mukherjee, R Petersen, HK Dressman, A Bild, J Koontz, ... New England Journal of Medicine 355 (6), 570-580, 2006 | 706 | 2006 |
| An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis A Sweet-Cordero, S Mukherjee, A Subramanian, H You, JJ Roix, ... Nature genetics 37 (1), 48-55, 2005 | 482 | 2005 |
| Molecular classification of multiple tumor types CH Yeang, S Ramaswamy, P Tamayo, S Mukherjee, RM Rifkin, M Angelo, ... Bioinformatics 17 (suppl_1), S316-S322, 2001 | 392 | 2001 |
| Timing of surgery following SARS‐CoV‐2 infection: an international prospective cohort study COVIDSurg Collaborative, GlobalSurg Collaborative, D Nepogodiev, ... Anaesthesia 76 (6), 748-758, 2021 | 369 | 2021 |
| General conditions for predictivity in learning theory T Poggio, R Rifkin, S Mukherjee, P Niyogi Nature 428 (6981), 419-422, 2004 | 356 | 2004 |
| Estimating dataset size requirements for classifying DNA microarray data S Mukherjee, P Tamayo, S Rogers, R Rifkin, A Engle, C Campbell, ... Journal of computational biology 10 (2), 119-142, 2003 | 344 | 2003 |
| Fast principal-component analysis reveals convergent evolution of ADH1B in Europe and East Asia KJ Galinsky, G Bhatia, PR Loh, S Georgiev, S Mukherjee, NJ Patterson, ... The American Journal of Human Genetics 98 (3), 456-472, 2016 | 303 | 2016 |
| Probability measures on the space of persistence diagrams Y Mileyko, S Mukherjee, J Harer Inverse Problems 27 (12), 124007, 2011 | 278 | 2011 |
| Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization S Mukherjee, P Niyogi, T Poggio, R Rifkin Advances in Computational Mathematics 25, 161-193, 2006 | 271* | 2006 |
| Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex D Tropea, G Kreiman, A Lyckman, S Mukherjee, H Yu, S Horng, M Sur Nature neuroscience 9 (5), 660-668, 2006 | 270 | 2006 |
| Support vector machine classification of microarray data S Mukherjee, P Tamayo, D Slonim, A Verri, T Golub, J Mesirov, T Poggio AI Memo 1677, Massachusetts Institute of Technology, 1999 | 269 | 1999 |
| Support vector method for multivariate density estimation V Vapnik, S Mukherjee Advances in neural information processing systems 12, 1999 | 268 | 1999 |
| Fréchet means for distributions of persistence diagrams K Turner, Y Mileyko, S Mukherjee, J Harer Discrete & Computational Geometry 52, 44-70, 2014 | 258 | 2014 |
| A phylogenetic transform enhances analysis of compositional microbiota data JD Silverman, AD Washburne, S Mukherjee, LA David Elife 6, e21887, 2017 | 255 | 2017 |
TOMASO POGGIOMcDermott Professor in Brain Sciences, MITEmail verificata su ai.mit.edu
