Integrating multiple-platform expression data through gene set features

Matej Holec, Filip Zelezny, Jiri Klema, Jakub Tolar

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

7 Scopus citations

Abstract

We demonstrate a set-level approach to the integration of multiple platform gene expression data for predictive classification and show its utility for boosting classification performance when singleplatform samples are rare. We explore three ways of defining gene sets, including a novel way based on the notion of a fully coupled .flux related to metabolic pathways. In two tissue classification tasks, we empirically show that the gene set based approach is useful for combining heterogeneous expression data, while surprisingly, in experiments constrained to a single platform, biologically meaningful gene sets acting as sample features are often outperformed by random gene sets with no biological relevance.

Original languageEnglish (US)
Title of host publicationBioinformatics Research and Applications - 5th International Symposium, ISBRA 2009, Proceedings
Pages5-17
Number of pages13
DOIs
StatePublished - Jul 15 2009
Event5th International Symposium on Bioinformatics Research and Applications, ISBRA 2009 - Fort Lauderdale, FL, United States
Duration: May 13 2009May 16 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5542
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Symposium on Bioinformatics Research and Applications, ISBRA 2009
CountryUnited States
CityFort Lauderdale, FL
Period5/13/095/16/09

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