User profiles for "author:Sayan Mukherjee"

Sayan Mukherjee

Duke Universiy, University of Leipzig, Max Planck Institute for Mathematics in the Sciences
Verified email at mis.mpg.de
Cited by 68569

[PDF][PDF] Optimal gene expression analysis by microarrays

LD Miller, PM Long, L Wong, S Mukherjee… - Cancer cell, 2002 - cell.com
DNA microarrays make possible the rapid and comprehensive assessment of the
transcriptional activity of a cell, and as such have proven valuable in assessing the …

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

A Subramanian, P Tamayo… - Proceedings of the …, 2005 - National Acad Sciences
Although genomewide RNA expression analysis has become a routine tool in biomedical
research, extracting biological insight from such information remains a major challenge …

Choosing multiple parameters for support vector machines

O Chapelle, V Vapnik, O Bousquet, S Mukherjee - Machine learning, 2002 - Springer
The problem of automatically tuning multiple parameters for pattern recognition Support
Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the …

Multiclass cancer diagnosis using tumor gene expression signatures

S Ramaswamy, P Tamayo, R Rifkin… - Proceedings of the …, 2001 - National Acad Sciences
The optimal treatment of patients with cancer depends on establishing accurate diagnoses
by using a complex combination of clinical and histopathological data. In some instances …

[HTML][HTML] Naught all zeros in sequence count data are the same

JD Silverman, K Roche, S Mukherjee… - Computational and …, 2020 - Elsevier
Genomic studies feature multivariate count data from high-throughput DNA sequencing
experiments, which often contain many zero values. These zeros can cause artifacts for …

Feature selection for SVMs

J Weston, S Mukherjee, O Chapelle… - Advances in neural …, 2000 - proceedings.neurips.cc
We introduce a method of feature selection for Support Vector Machines. The method is
based upon finding those features which minimize bounds on the leave-one-out error. This …

Nonlinear prediction of chaotic time series using support vector machines

S Mukherjee, E Osuna, F Girosi - Neural Networks for Signal …, 1997 - ieeexplore.ieee.org
A novel method for regression has been recently proposed by Vapnik et al.(1995, 1996).
The technique, called support vector machine (SVM), is very well founded from the …

[HTML][HTML] A genomic strategy to refine prognosis in early-stage non–small-cell lung cancer

A Potti, S Mukherjee, R Petersen… - … England Journal of …, 2006 - Mass Medical Soc
Background Clinical trials have indicated a benefit of adjuvant chemotherapy for patients
with stage IB, II, or IIIA—but not stage IA—non–small-cell lung cancer (NSCLC). This …

An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis

A Sweet-Cordero, S Mukherjee, A Subramanian… - Nature …, 2005 - nature.com
Using advanced gene targeting methods, generating mouse models of cancer that
accurately reproduce the genetic alterations present in human tumors is now relatively …

General conditions for predictivity in learning theory

T Poggio, R Rifkin, S Mukherjee, P Niyogi - Nature, 2004 - nature.com
Developing theoretical foundations for learning is a key step towards understanding
intelligence.'Learning from examples' is a paradigm in which systems (natural or artificial) …