Inferring Pathway Dysregulation in Cancers from Multiple Types of Omic Data

Authors

Shelley M. MacNeil, University of Utah
William E. Johnson, University of Utah
Dean Y. Li, University of Utah
Stephen R. Piccolo, Brigham Young University
Andrea H. Bild, University of Utah

Keywords

Pathway dysregulation, Cancer, Omic Data

Abstract

Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common. Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level. GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns. We compare GSOA to alternative methods and demonstrate its ability to identify pathways known to play a role in various cancer phenotypes. Software implementing the GSOA method is freely available from https://bitbucket.org/srp33/gsoa.

Original Publication Citation

MacNeil SM, Johnson WE, Li DY, Piccolo SR*, Bild AH. “Inferring pathway dysregulation in cancers from multiple types of omic data”. Genome Medicine, 2015 Jun 26;7(1):61

BYU ScholarsArchive Citation

MacNeil, Shelley M.; Johnson, William E.; Li, Dean Y.; Piccolo, Stephen R.; and Bild, Andrea H., "Inferring Pathway Dysregulation in Cancers from Multiple Types of Omic Data" (2015). Faculty Publications. 7464.
https://scholarsarchive.byu.edu/facpub/7464

Document Type

Peer-Reviewed Article

Publication Date

2015-06-26

Publisher

BioMed Central

Language

English

College

Life Sciences

Department

Biology

University Standing at Time of Publication

Associate Professor

Copyright Use Information

https://lib.byu.edu/about/copyright/