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Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae

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Abstract

At the chromosomal level of evolution, recombination is a major factor for genetic variations. However, recombination does not occur with equal frequency at various regions of genome. The recombination has the tendency to occur at specific region with higher frequency and with low frequency at other regions, and former regions are named as hot recombination regions whereas later are called cold regions for recombination. In this paper, we have developed supervised machine learning-based models using artificial neural network, support vector machine and Naïve Bayes for efficient and effective classification of such hot and cold recombination regions based on the nucleotide composition of sequences. All models were validated and tested using tenfold cross-validation. Furthermore, neural network model was validated using leave one out and random sampling techniques in addition to tenfold cross-validation. Moreover, models were evaluated using receiver-operating curve. Our results indicate that artificial neural network achieves the best result.

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Acknowledgments

The authors are highly grateful to Department of Biotechnology, New Delhi for providing support for this work under Bioinformatics Infrastructure Facility of DBT at MANIT Bhopal.

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Authors and Affiliations

  1. Department of Bioinformatics, Mathematics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal, MP, 462003, India

    Ashok Kumar Dwivedi

  2. Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal, MP, 462003, India

    Ashok Kumar Dwivedi & Usha Chouhan

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  1. Ashok Kumar Dwivedi
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  2. Usha Chouhan
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Correspondence to Ashok Kumar Dwivedi.

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Dwivedi, A.K., Chouhan, U. Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae . Neural Comput & Applic 29, 529–535 (2018). https://doi.org/10.1007/s00521-016-2466-6

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  • DOI: https://doi.org/10.1007/s00521-016-2466-6

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