NDLI: Revealing miRNA Regulation and miRNA Target Prediction Using Constraint-Based Learning

Content Provider	IEEE Xplore Digital Library
Author	Alshalalfa, M. Tan, M. Naji, G. Alhajj, R. Polat, F. Rokne, J.
Copyright Year	1998
Abstract	The past decades have witnessed advances in genomic technology; and this has allowed laboratories to generate vast amount of biological data, including microarray gene expression data. Effective analysis of the data helps in better understanding the mechanisms behind the complex behavior of the cell. Actually, a huge body of research focuses on the role of gene regulatory networks (GRNs) in controlling the cell. However, studying the heterogeneous interactions between mRNA and miRNA has received less attention. Fortunately, revealing the targets of miRNAs started to gain some consideration from the research community. Further, integrating mRNA gene expression and miRNA expression data is receiving more attention; the target is to understand the role of miRNA in regulating mRNA in different cell contexts; this could lead to predicting miRNA targets and constructing miRNA-mRNA interaction networks. On the other hand, we have already demonstrated the power of constraint-based learning as a promising technique to learn the structure of GRN , which are homogeneous in the sense that they contain one type of nodes, namely, genes. In this study, we extend our previous work to show how constraint-based learning can be effectively applied to tackle a more challenging problem, namely, to learn the structure of heterogeneous networks, like mRNA-miRNA network. In other words, to build the whole picture of the heterogeneous interactions, we used constraint-based learning algorithms which usually perform well on sparse graphs to predict the interactions within heterogeneous networks, namely, miRNA-mRNA interactions. We are able to achieve this by extending our PCPDPr algorithm, which works on homogeneous networks. The extended version named htrPCPDPr is capable of handling networks connecting two heterogeneous sets of nodes into a bipartite graph. This way, we propose a new learning mechanism to predict miRNA targets from expression profiles of both mRNA and miRNA, in addition to sequence-based prior knowledge about the interactions. The method has been applied to different set of genes related to the Alzheimer disease; the results reported in this paper demonstrate the novelty, applicability, and effectiveness of the proposed approach.
Starting Page	1354
Ending Page	1364
Page Count	11
File Size	562120
File Format	PDF
ISSN	10946977
Volume Number	42
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Alzheimer's disease RNA Correlation Prediction algorithms Proteins miRNA regulation Constraint-based learning expression profiles gene regulation interaction networks microRNA
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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