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BioMed Research International
Volume 2014 (2014), Article ID 154594, 8 pages
http://dx.doi.org/10.1155/2014/154594
Research Article

An Unsupervised Approach to Predict Functional Relations between Genes Based on Expression Data

Computational Systems Biology Lab, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan

Received 1 November 2013; Revised 31 January 2014; Accepted 3 February 2014; Published 31 March 2014

Academic Editor: Farit Mochamad Afendi

Copyright © 2014 Md. Altaf-Ul-Amin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This work presents a novel approach to predict functional relations between genes using gene expression data. Genes may have various types of relations between them, for example, regulatory relations, or they may be concerned with the same protein complex or metabolic/signaling pathways and obviously gene expression data should contain some clues to such relations. The present approach first digitizes the log-ratio type gene expression data of S. cerevisiae to a matrix consisting of 1, 0, and −1 indicating highly expressed, no major change, and highly suppressed conditions for genes, respectively. For each gene pair, a probability density mass function table is constructed indicating nine joint probabilities. Then gene pairs were selected based on linear and probabilistic relation between their profiles indicated by the sum of probability density masses in selected points. The selected gene pairs share many Gene Ontology terms. Furthermore a network is constructed by selecting a large number of gene pairs based on FDR analysis and the clustering of the network generates many modules rich with similar function genes. Also, the promoters of the gene sets in many modules are rich with binding sites of known transcription factors indicating the effectiveness of the proposed approach in predicting regulatory relations.