NDLI: K4. Gene network construction and pathways analysis for high throughput microarrays

Content Provider	IEEE Xplore Digital Library
Author	Samee, N.M.A. Solouma, N.H. Kadah, Y.M.
Copyright Year	2012
Description	Author affiliation: Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt (Kadah, Y.M.) \|\| Computer Engineering Department, Misr University for Science and Technology, Egypt (Samee, N.M.A.) \|\| Engineering Applications Department, NILES, Cairo University, Giza, Egypt (Solouma, N.H.)
Abstract	The key idea discussed in this paper is to infer gene regulatory network from high throughput microarray data for Hepatocellular Carcinoma (HCC). Working with such huge number of genes is a complex process. So, our framework for inferring gene interactions from large scale microarrays is based on a selected set of informative genes. We applied two measures of dependencies between genes: Correlation and mutual information. Therefore, two types of networks were constructed: Co-expression network and Mutual information network. Some Mutual information network inference algorithms: Context Likelihood of Relatedness (CLR), Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Minimum Redundancy Network (MRNET) were applied. A proposed method for simplifying the complex structure of the inferred network is introduced using the Minimum Spanning Tree (MST) which provides a better visual interpretation of the constructed networks. From the constructed networks we were able to identify a set of functional gene modules. These modules were validated using the Gene Ontology (GO) enrichment. The GO enrichment analysis has proven the strength of the ARACNE inference algorithm over all other employed algorithms. Moreover, a comparison was carried out between the Mutual information network inference and the well known Bayesian inference. To establish this comparison, specific pathways in HCC were rather chosen. These pathways were tested for their significance using singular value decomposition. According to this comparison, again the ARACNE showed better results.
Starting Page	649
Ending Page	658
File Size	1224400
Page Count	10
File Format	PDF
ISBN	9781467318846
e-ISBN	9781467318877
DOI	10.1109/NRSC.2012.6208578
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-10
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mutual information Correlation Bayesian methods Educational institutions Inference algorithms Uncertainty Heuristic algorithms Singular Value Decomposition Gene Co-expression Network Mutual Information Network Bayesian Network Pathways Analysis
Content Type	Text
Resource Type	Article

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