iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples

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• 作者：Muhammad Kabir ; Maqsood Hayat
• 关键词：TNC ; DNC ; DNA ; SVM ; PNN
• 刊名：Molecular Genetics and Genomics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：291
• 期：1
• 页码：285-296
• 全文大小：919 KB
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• 作者单位：Muhammad Kabir (1)
  Maqsood Hayat (1)
  
  1. Department of Computer Science, Abdul Wali Khan University, Mardan, KP, Pakistan
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Cell Biology
  Biochemistry
  Microbial Genetics and Genomics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1617-4623

文摘

Meiotic recombination is vital for maintaining the sequence diversity in human genome. Meiosis and recombination are considered the essential phases of cell division. In meiosis, the genome is divided into equal parts for sexual reproduction whereas in recombination, the diverse genomes are combined to form new combination of genetic variations. Recombination process does not occur randomly across the genomes, it targets specific areas called recombination “hotspots” and “coldspots”. Owing to huge exploration of polygenetic sequences in data banks, it is impossible to recognize the sequences through conventional methods. Looking at the significance of recombination spots, it is indispensable to develop an accurate, fast, robust, and high-throughput automated computational model. In this model, the numerical descriptors are extracted using two sequence representation schemes namely: dinucleotide composition and trinucleotide composition. The performances of seven classification algorithms were investigated. Finally, the predicted outcomes of individual classifiers are fused to form ensemble classification, which is formed through majority voting and genetic algorithm (GA). The performance of GA-based ensemble model is quite promising compared to individual classifiers and majority voting-based ensemble model. iRSpot-GAEnsC has achieved 84.46 % accuracy. The empirical results revealed that the performance of iRSpot-GAEnsC is not only higher than the examined algorithms but also better than existing methods in the literature developed so far. It is anticipated that the proposed model might be helpful for research community, academia and for drug discovery. Keywords TNC DNC DNA SVM PNN