Network propagation models for gene selection

Wei Zhang, Baryun Hwang, Baolin Wu, Rui Kuang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we explore several network propagation methods for gene selection from microarray gene expression datasets. The network propagation methods capture gene co-expression and differential expression with unified machine learning frameworks. Large scale experiments on five breast cancer datasets validated that the network propagation methods are capable of selecting genes that are more biologically interpretable and more consistent across multiple datasets, compared with the existing approaches.

Original languageEnglish (US)
Title of host publication2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
DOIs
StatePublished - Dec 1 2010
Event2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010 - Cold Spring Harbor, NY, United States
Duration: Nov 10 2010Nov 12 2010

Publication series

Name2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010

Other

Other2010 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2010
CountryUnited States
CityCold Spring Harbor, NY
Period11/10/1011/12/10

Keywords

  • Biomarkers
  • Breast cancer metastasis
  • Gene expression
  • Network propagation

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