Proposing a novel community detection approach to identify co-interacting genomic regions

Mohammadjavad Hosseinpoor, Hamid Parvin, Samad Nejatian, Vahideh Rezaie, Karamollah Bagherifard, Abdollah Dehzangi, Amin Beheshti, Hamid Alinejad-Rokny

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2193-2217
Number of pages25
JournalMathematical Biosciences and Engineering
Issue number3
StatePublished - Jan 13 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Agricultural and Biological Sciences(all)
  • Computational Mathematics
  • Applied Mathematics


  • Community detection
  • Genomic interacting regions
  • Genomics graph interaction
  • Health data analytics
  • Modularity
  • Multi-objective optimization

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