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, 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
Advances in neural information processing systems 13, 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
Tests of general relativity with GW170817
BP Abbott, R Abbott, TD Abbott, F Acernese, K Ackley, C Adams, T Adams, ...
Physical review letters 123 (1), 011102, 2019
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, ...
ISMB (Supplement of Bioinformatics) 2001, 316-322, 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
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
Fréchet means for distributions of persistence diagrams
K Turner, Y Mileyko, S Mukherjee, J Harer
Discrete & Computational Geometry 52, 44-70, 2014
A phylogenetic transform enhances analysis of compositional microbiota data
JD Silverman, AD Washburne, S Mukherjee, LA David
Elife 6, e21887, 2017
Support vector method for multivariate density estimation
V Vapnik, S Mukherjee
Advances in neural information processing systems 12, 1999
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
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, 161-193, 2006
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