Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae
| Autor: | Usha Chouhan, Ashok Kumar Dwivedi |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Artificial neural network biology business.industry Computer science Saccharomyces cerevisiae Chromosome Pattern recognition biology.organism_classification Genome Nucleotide composition Support vector machine 03 medical and health sciences Naive Bayes classifier 030104 developmental biology Artificial Intelligence Genetic variation Artificial intelligence business Software Recombination |
| Zdroj: | Neural Computing and Applications. 29:529-535 |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | 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 Naive 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. |
| Databáze: | OpenAIRE |
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