Reconstruction of Dominant Gene Regulatory Network from Microarray Data Using Rough Set and Bayesian Approach
- *Corresponding Author:
- Sudip Mandal
Head of the Department, ECE Department
Global Institute of Management and Technology, Krishna Nagar, India
E-mail: [email protected]
Received date: August 22, 2013; Accepted date: September 24, 2013; Published date: September 30, 2013
Citation: Mandal S, Saha G, Pal RK (2013) Reconstruction of Dominant Gene Regulatory Network from Microarray Data Using Rough Set and Bayesian Approach. J Comput Sci Syst Biol 6:262-270. doi:10.4172/jcsb.1000121
Copyright: © 2013 Mandal S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Biological databases, containing genetic information of patients, are undergoing tremendous growth beyond our analysing capability. However such analysis can reveal new findings about the cause and subsequent treatment of any disease. Interactions between genes and the proteins they synthesize shape Genetic Regulatory Networks (GRN). In this context, it has been developed a model capable of representing small dominant GRN, combining characteristics from the Rough Set and Bayesian Network. The investigation has been carried out on the publicly available microarray dataset for Lung Adenocarcinoma, obtained from the National Center for Biotechnology Information (NCBI) website. The analysis revealed that Rough Set Theory (RST) is able to extract the various dominant genes in term of reducts which play an important role in causing the disease and also able to provide a unique simplified rule set for building expert systems in medical sciences with high accuracy and coverage factor. The next part of this work is based on reconstruction of GRN using Bayesian network, which is a mathematical tool for modelling conditional independences between stochastic variables like different gene expression. This proposed Bayesian approach using scaled mutual information for scoring is applied to the dataset corresponding to most dominant responsible genes for Adenocarcinoma to uncover, gene/protein interactions and key biological features of the cellular system. Finally different interacting regulatory path which are the gene signature for a particular disease, between dominating genes are inferred from the probability distribution table and Bayesian Graph. Such reconstructed regulatory network is attractive for their ability to describe complex stochastic processes like gene transcription, classification of biological sequencing and intuitive model of causal influence successfully. This may serve as a signature pattern of the disease Adenocarcinoma, which has been extracted from huge microarray dataset. Extraction of this signature pattern is very useful for diagnosis of this disease.