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Microarrays (Basel). 2015 Jun;4(2):287-310. doi: 10.3390/microarrays4020287.

An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.

Author information

1
Bio IE Lab, The Applied Optimization Group at UPRM, Industrial Engineering Department, University of Puerto Rico at Mayaguez, Call Box 9000, Mayagüez, PR 00681, USA.
2
Pittsburgh Supercomputing Center, 300 S. Craig Street, Pittsburgh, PA 15213, USA.
3
Bio IE Lab, The Applied Optimization Group at UPRM, Industrial Engineering Department, University of Puerto Rico at Mayaguez, Call Box 9000, Mayagüez, PR 00681, USA ; Department of Pharmacology and Toxicology, Ponce School of Medicine, PO Box 700, Ponce, PR 00732, USA.

Abstract

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

KEYWORDS:

cancer biology; signaling pathways; traveling salesman problem

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