Reverse engineering gene networks using singular value decomposition and robust regression

M. K.Stephen Yeung, Jesper Tegner, James J. Collins*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

499 Scopus citations

Abstract

We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expresion data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses robust regression to identify the solution with the smallest number of connections as the most likely solution. Our algorithm has O(log N) sampling complexity and O(N4) computational complexity. We test and validate our approach in a series of in numero experiments on model gene networks.

Original languageEnglish (US)
Pages (from-to)6163-6168
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume99
Issue number9
DOIs
StatePublished - Apr 30 2002

ASJC Scopus subject areas

  • General

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