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Mathematical Programming Approaches for the Analysis of Microarray Data
 

Summary: 1
Mathematical Programming Approaches for
the Analysis of Microarray Data
Ioannis P. Androulakis
Biomedical Engineering Department and Chemical and Biochemical Engineering
Department, Rutgers, The State University of New Jersey, Piscataway, New Jersey
08854
yannis@rci.rutgers.edu
Abstract. One of the major challenges facing the analysis of high-throughput mi-
croarray measurements is how to extract in a systematic and rigorous way the bio-
logically relevant components from the experiments in order to establish meaningful
connections linking genetic information to cellular function. Because of the signif-
icant amount of experimental information that is generated (expression levels of
thousands of genes), computer-assisted knowledge extraction is the only realistic
alternative for managing such an information deluge. Mathematical programming
offers an interesting alternative for the development of systematic methodologies
aiming towards such an analysis. We summarize recent developments related to
critical problems in the analysis of microarray data, namely: tissue clustering and
classification, informative gene selection and reverse engineering of gene regulatory
networks. We demonstrate how advances in non linear and mixed-integer optimiza-

  

Source: Androulakis, Ioannis (Yannis) - Biomedical Engineering Department & Department of Chemical and Biochemical Engineering, Rutgers University

 

Collections: Engineering; Biology and Medicine