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has been cited by the following article:

• TITLE: Cross Entropy Based Sparse Logistic Regression to Identify Phenotype-Related Mutations in Methicillin-Resistant Staphylococcus aureus

  AUTHORS: Bahriddin Abapihi, Mohammad Reza Faisal, Ngoc Giang Nguyen, Mera Kartika Delimayanti, Bedy Purnama, Favorisen Rosyking Lumbanraja, Dau Phan, Mamoru Kubo, Kenji Satou

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

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.13 No.7, July 30, 2020

  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.