|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 (1-3), 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, 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, 668-674, 2001
|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
|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, 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, 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
|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, 2006
|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
|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
|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 (1-3), 161-193, 2006
|Probability measures on the space of persistence diagrams|
Y Mileyko, S Mukherjee, J Harer
Inverse Problems 27 (12), 124007, 2011
|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
|Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer|
CR Acharya, DS Hsu, CK Anders, A Anguiano, KH Salter, KS Walters, ...
Jama 299 (13), 1574-1587, 2008
|Positron emission tomography scanning with 2-fluoro-2-deoxy-d-glucose as a predictor of response of neoadjuvant treatment for non-small cell carcinoma|
RJ Cerfolio, B Ojha, S Mukherjee, AH Pask, CS Bass, CR Katholi
The Journal of thoracic and cardiovascular surgery 125 (4), 938-944, 2003
|Fréchet means for distributions of persistence diagrams|
K Turner, Y Mileyko, S Mukherjee, J Harer
Discrete & Computational Geometry 52 (1), 44-70, 2014