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Cross Entropy Based Sparse Logistic Regression to Identify Phenotype-Related Mutations in Methicillin-Resistant <i>Staphylococcus aureus</i>
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 1 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 2 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 3 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 4 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 5 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 6 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 7 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 8 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 9 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Journal of Biomedical Science and Engineering·Volume 13 (2020)·Pages 168–174·Published 24 July 2020·DOI10.4236/jbise.2020.137016
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Abstract
Emergence of drug resistant bacteria is one of the serious problems in today’s public health. However, the relationship between genomic mutation of bacteria and the phenotypic difference of them is still unclear. In this paper, based on the mutation information in whole genome sequences of 96 MRSA strains, two kinds of phenotypes (pathogenicity and drug resistance) were learnt and predicted by machine learning algorithms. As a result of effective feature selection by cross entropy based sparse logistic regression, these phenotypes could be predicted in sufficiently high accuracy (100% and 97.87%, respectively) with less than 10 features. It means that we could develop a novel rapid test method in the future for checking MRSA phenotypes.
KeywordsMRSAPhenotype ClassificationFeature SelectionHigh-Dimensional Binary DataCross Entropy
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