A Computational Inference Framework for analyzing Gene Regulation Pathway using Microarray Data

Microarray experiments produce gene expression data at such a high speed and volume that it is imperative to use highly specialized computational tools for their analyses. One group of such computational tools deals with, namely, “meta-analysis” of microarray data. This step attempts to extract biological interpretations from the identified gene expression pattern. One particular aspect of meta-analysis is sorting out which gene regulation pathways are active and/or inhibited. The focus on this paper is to propose a computational framework with which scientists can compare microarray data with known gene regulation networks that are formed by two known binary gene regulation relationships, activate and inhibit. Using this framework scientists can conduct numerous analysis tasks including (i) identify active or inhibited sub-networks out of massively interconnected gene regulation pathways, (ii) find key genes, namely hubs, that are inferred to be widely involved in multiple aspec...

• Type: Conference
• Year: 2006
• Where: BIBE
• Authors: Dong-Guk Shin, John Bluis, Yoo Ah Kim, Winfried Krueger, Jeffrey Maddox, Ravi Nori, Nathan Viniconis, Hsin-Wei Wang, Alan Wong, David W. Rowe
• Keywords: BIBE 2006, Bioinformatics, Gene Expression, Gene Regulation, Gene Regulation Pathways