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Proposing a novel community detection approach to identify cointeracting genomic regions

1 Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj 1979124119, Iran
2 Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani 1979123114, Iran
3 Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani 1979123114, Iran
4 Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj 1979124119, Iran
5 Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj 1979124119, Iran
6 Department of Mathematics, Yasooj Branch, Islamic Azad University, Yasooj 1979124119, Iran
7 Department of Computer Science, Morgan State University, Baltimore 21251, United States
8 Department of Computing, Macquarie University, Sydney 2109, Australia
9 Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
10 School of Computer Science and Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia

Special Issues: Application of Soft Computing

Modern next generation sequencing technologies produce huge amounts of genome-wide data that allow researchers to have a deeper understanding of genomics of organisms. Despite these huge amounts of data, our understanding of the transcriptional regulatory networks is still incomplete. Conformation dependent chromosome interaction maps technologies (Hi-C) have enabled us to detect elements in the genome which interact with each other and regulate the genes. Summarizing these interactions as a data network leads to investigation of the most important properties of the 3D genome structure such as gene co-expression networks. In this work, a Pareto-Based Multi-Objective Optimization algorithm is proposed to detect the co-expressed genomic regions in Hi-C interactions. The proposed method uses fixed sized genomic regions as the vertices of the graph. Number of read between two interacting genomic regions indicate the weight of each edge. The performance of our proposed algorithm was compared to the Multi-Objective PSO algorithm on five networks derived from cis genomic interactions in three Hi-C datasets (GM12878, CD34+ and ESCs). The experimental results show that our proposed algorithm outperforms Multi-Objective PSO technique in the identification of co-interacting genomic regions.
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Keywords community detection; genomics graph interaction; modularity; multi-objective optimization; health data analytics; genomic interacting regions

Citation: Mohammadjavad Hosseinpoor, Hamid Parvin, Samad Nejatian, Vahideh Rezaie, Karamollah Bagherifard, Abdollah Dehzangi, Amin Beheshti, Hamid Alinejad-Rokny. Proposing a novel community detection approach to identify cointeracting genomic regions. Mathematical Biosciences and Engineering, 2020, 17(3): 2193-2217. doi: 10.3934/mbe.2020117

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