Outlier Analysis and Top Scoring Pairfor Integrated Data Analysisand Biomarker Discovery

Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression,...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 11; no. 3; pp. 520 - 532
Main Authors: Ochs, Michael F, Farrar, Jason E, Considine, Michael, Wei, Yingying, Meshinchi, Soheil, Arceci, Robert J
Format: Journal Article
Language:English
Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.05.2014
Subjects:
Algorithms
Biomarkers
Carcinogens
Deregulation
Drugs
Genes
Medical treatment
Pathways
Pediatrics
Tumors
ISSN:1545-5963, 1557-9964
Online Access:Get full text
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Summary:Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression, and gene amplifications or deletions. We demonstrate a novel approach that identifies pathways of interest by integrating outlier analysis within and across molecular data types with gene set analysis. We use the results to seed the top-scoring pair algorithm to identify robust biomarkers associated with pathway deregulation. 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 pediatric AML tumors.
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ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2013.153