Sayan Mukherjee
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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
Choosing multiple parameters for support vector machines
O Chapelle, V Vapnik, O Bousquet, S Mukherjee
Machine learning 46 (1), 131-159, 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
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
Feature selection for SVMs
J Weston, S Mukherjee, O Chapelle, M Pontil, T Poggio, V Vapnik
Neural Information Processing Systems Foundation, 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
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
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
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
General conditions for predictivity in learning theory
T Poggio, R Rifkin, S Mukherjee, P Niyogi
Nature 428 (6981), 419-422, 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
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
Support vector method for multivariate density estimation
S Mukherjee, V Vapnik
Center for biological and computational learning. Department of Brain and …, 1999
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
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 (1), 161-193, 2006
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
Probability measures on the space of persistence diagrams
Y Mileyko, S Mukherjee, J Harer
Inverse Problems 27 (12), 124007, 2011
Androgen-induced differentiation and tumorigenicity of human prostate epithelial cells
R Berger, PG Febbo, PK Majumder, JJ Zhao, S Mukherjee, S Signoretti, ...
Cancer research 64 (24), 8867-8875, 2004
Optimal gene expression analysis by microarrays
LD Miller, PM Long, L Wong, S Mukherjee, LM McShane, ET Liu
Cancer cell 2 (5), 353-361, 2002
Fréchet means for distributions of persistence diagrams
K Turner, Y Mileyko, S Mukherjee, J Harer
Discrete & Computational Geometry 52 (1), 44-70, 2014
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