Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 43, Issue 6, Pages 932-944Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2010.07.001
Keywords
Microarrays; Independent component analysis; Data mining; Parthenolide; Gene modules
Funding
- NIH [5U01GM061374-08]
- NSF [CNS-0619926]
- Stanford Bio-X Undergraduate Research Award
- Geraldine Jackson Fuhrman Stanford Graduate Fellowship
- HHMI
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As public microarray repositories rapidly accumulate gene expression data, these resources contain increasingly valuable information about cellular processes in human biology This presents a unique opportunity for intelligent data mining methods to extract information about the transcriptional modules underlying these biological processes Modeling cellular gene expression as a combination of functional modules, we use Independent component analysis (ICA) to derive 423 fundamental components of human biology from a 9395-array compendium of heterogeneous expression data Annotation using the Gene Ontology (GO) suggests that while sonic of these components represent known biological modules, others may describe biology not well characterized by existing manually-curated ontologies In order to understand the biological functions represented by these modules, we investigate the mechanism of the preclinical anti-cancer drug parthenolide (PTL) by analyzing the differential expression of our fundamental components Our method correctly identifies known pathways and predicts that N-glycan biosynthesis and T-cell receptor signaling may contribute to PTL response The fundamental gene modules we describe have the potential to provide pathway-level insight into new gene expression datasets (C) 2010 Elsevier Inc All rights reserved
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