Volume 5, Issue 2 (Summer 2018)
jhbmi 2018, 5(2): 304-313 |
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Ph.D. in Electrical Engineering, Assistant Professor, Electrical Engineering Dept., Yasooj Branch, Islamic Azad University, Yasooj, Iran
Abstract: (4886 Views)
Introduction: Cancer, as one of the most common diseases, has influenced the health of many people. The main aim of this study was to present a multi-objective evolutionary algorithm. The algorithm is capable of detecting and extracting potentially promoter/enhancer areas in the chromosomes of the affected people using the information concerning inter-genomic interactions. The correct extraction of these areas can help early diagnosis of cancer.
Methods: In this applied and descriptive research, Hi-C data set including information on intergenomic interactions in the GM12878 cell was used. Multi-objective evolutionary algorithm was used in order to discover and extract potential promoter /enhancer interactions. The mentioned algorithm was implemented using MATLAB software. Furthermore, the efficiency of this algorithm was evaluated using two criteria. The first criterion is a proportional function that calculates the magnitude of inter-genomic interactions relative to the length of the genome regions; and the second criterion is the number of discovered potential promoters/enhancers.
Results: The results and comparisons showed higher efficiency and optimality of the suggested method in discovering promoter/Enhancer interactions with variable length in comparison to HiCPro method. Therefore, the suggested method is able to discover the potential promoter/ enhancer interactions that cannot be discovered by HiC-Pro method.
Conclusion: The suggested algorithm is able to optimally discover and extract potential promoter/ enhancer with variable length. This is a great help in medical science for early diagnosis of cancer.
Methods: In this applied and descriptive research, Hi-C data set including information on intergenomic interactions in the GM12878 cell was used. Multi-objective evolutionary algorithm was used in order to discover and extract potential promoter /enhancer interactions. The mentioned algorithm was implemented using MATLAB software. Furthermore, the efficiency of this algorithm was evaluated using two criteria. The first criterion is a proportional function that calculates the magnitude of inter-genomic interactions relative to the length of the genome regions; and the second criterion is the number of discovered potential promoters/enhancers.
Results: The results and comparisons showed higher efficiency and optimality of the suggested method in discovering promoter/Enhancer interactions with variable length in comparison to HiCPro method. Therefore, the suggested method is able to discover the potential promoter/ enhancer interactions that cannot be discovered by HiC-Pro method.
Conclusion: The suggested algorithm is able to optimally discover and extract potential promoter/ enhancer with variable length. This is a great help in medical science for early diagnosis of cancer.
Keywords: Promoter, Enhancer, Hi-C Dataset, Multi-Objective Simulation Annealing Algorithm (MOSA), HiC- Pro Method
Type of Study: Original Article |
Subject:
Bioinformatics
Received: 2017/10/22 | Accepted: 2018/06/12
Received: 2017/10/22 | Accepted: 2018/06/12
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