Abapihi, Bahriddin and Faisal, Mohammad Reza and Nguyen, Ngoc G and Delimayanti, Mera K and Purnama, Bedy and Lumbanraja, Favorisen R and Phan, Dau and Kubo, Mamoru and Satou, Kenji (2020) Cross Entropy Based Sparse Logistic Regression to Identify Phenotype-Related Mutations in Methicillin-Resistant Staphylococcus aureus. Journal of Biomedical Science and Engineering, 13 (7). pp. 168-174. ISSN 1937-6871

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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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer
Depositing User:	Favorisen R Lumbanraja
Date Deposited:	24 Feb 2021 05:04
Last Modified:	24 Feb 2021 05:04
URI:	http://repository.lppm.unila.ac.id/id/eprint/28065

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