Discovering non-exclusive functional modules from gene expression data

Authors:
Debahuti Mishra;Kailash Shaw;Sashikala Mishra;Amiya Kumar Rath;Milu Acharya
Affiliations:
Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha O Anusandhan Deemed to be University, Jagamohan Nagar, Khandagiri, Bhubaneswar, Odisha, Pin ...;Department of Computer Science and Engineering, Gandhi Engineering College, Madanpur Post, Bhubaneswar, Odisha, Pin-752054, India.;Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha O Anusandhan Deemed to be University, Jagamohan Nagar, Khandagiri, Bhubaneswar, Odisha, Pin ...;Department of Computer Science and Engineering, College of Engineering Bhubaneswar, Plot-1, Sector-B, CNI Complex, Patia, Bhubaneswar, Odisha, Pin-751024, India.;Department of Mathematics, Institute of Technical Education and Research, Siksha O Anusandhan Deemed to be University, Jagamohan Nagar, Khandagiri, Bhubaneswar, Odisha, Pin-751030, India
Venue:
International Journal of Information and Communication Technology
Year:
2011

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Abstract

Biological processes are not independent of each other as genes participate in multiple different processes. Each gene should be assigned to multiple biclusters. In real life, more than one gene is responsible for a particular type of disease. The biclustering can associate clusters with gene arrangement patterns, preserving genomic information. Additionally, overlapping capability is desirable for the discovery of multiple conserved patterns within a single genome. In strict or crisp partition-based biclustering, each gene/condition belongs to exactly one functional module whereas, addressing some biological questions requires partitioning methods leading to non-exclusive functional modules. The proposed method involves a novel strategy to discover such non-exclusive pattern-based biclusters using fuzzy set approach. We have evaluated the performance of our proposed model with few existing ones and the result shows that this can be suitable for application to genomes with high genetic exchange and various conserved gene arrangements in gene regulatory networks.