Outlier gene set analysis combined with top scoring pair provides robust biomarkers of pathway activity

Michael F. Ochs, Jason E. Farrar, Michael Considine, Yingying Wei, Soheil Meschinchi, Robert J. Arceci

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

Abstract

Cancer is a disease driven by pathway activity, while useful biomarkers to predict outcome (prognostic markers) or determine treatment (treatment markers) rely on individual genes, proteins, or metabolites. We provide a novel approach that isolates pathways of interest by integrating outlier analysis and gene set analysis and couple it to the top-scoring pair algorithm to identify robust biomarkers. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed AML tumors.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages47-58
Number of pages12
Volume7986 LNBI
DOIs
StatePublished - 2013
Event8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013 - Nice, France
Duration: Jun 17 2013Jun 20 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7986 LNBI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013
CountryFrance
CityNice
Period6/17/136/20/13

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Outlier gene set analysis combined with top scoring pair provides robust biomarkers of pathway activity'. Together they form a unique fingerprint.

Cite this