Author(s)

Bahriddin Abapihi¹, Mohammad Reza Faisal¹, Ngoc Giang Nguyen¹, Mera Kartika Delimayanti¹, Bedy Purnama¹, Favorisen Rosyking Lumbanraja¹, Dau Phan¹, Mamoru Kubo², Kenji Satou²

¹Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan.
²Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan.

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.

MRSA, Phenotype Classification, Feature Selection, High-Dimensional Binary Data, Cross Entropy

Abapihi, B. , Faisal, M. , Nguyen, N. , Delimayanti, M. , Purnama, B. , Lumbanraja, F. , Phan, D. , Kubo, M. and Satou, K. (2020) Cross Entropy Based Sparse Logistic Regression to Identify Phenotype-Related Mutations in Methicillin-Resistant Staphylococcus aureus. Journal of Biomedical Science and Engineering, 13, 168-174. doi: 10.4236/jbise.2020.137016.