PyBact: an algorithm for bacterial identification

PyBact is a software written in Python for bacterial identification. The code simulates the predefined behavior of bacterial species by generating a simulated data set based on the frequency table of biochemical tests from diagnostic microbiology textbook. The generated data was used for predictive...

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Detalles Bibliográficos
Autores principales: Nantasenamat, Chanin, Preeyanon, Likit, Isarankura-Na-Ayudhya, Chartchalerm, Prachayasittikul, Virapong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Leibniz Research Centre for Working Environment and Human Factors 2011
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109015/
https://www.ncbi.nlm.nih.gov/pubmed/27857678
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author Nantasenamat, Chanin
Preeyanon, Likit
Isarankura-Na-Ayudhya, Chartchalerm
Prachayasittikul, Virapong
author_facet Nantasenamat, Chanin
Preeyanon, Likit
Isarankura-Na-Ayudhya, Chartchalerm
Prachayasittikul, Virapong
author_sort Nantasenamat, Chanin
collection PubMed
description PyBact is a software written in Python for bacterial identification. The code simulates the predefined behavior of bacterial species by generating a simulated data set based on the frequency table of biochemical tests from diagnostic microbiology textbook. The generated data was used for predictive model construction by machine learning approaches and results indicated that the classifiers could accurately predict its respective bacterial class with accuracy in excess of 99 %.
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spelling pubmed-51090152016-11-17 PyBact: an algorithm for bacterial identification Nantasenamat, Chanin Preeyanon, Likit Isarankura-Na-Ayudhya, Chartchalerm Prachayasittikul, Virapong EXCLI J Original Article PyBact is a software written in Python for bacterial identification. The code simulates the predefined behavior of bacterial species by generating a simulated data set based on the frequency table of biochemical tests from diagnostic microbiology textbook. The generated data was used for predictive model construction by machine learning approaches and results indicated that the classifiers could accurately predict its respective bacterial class with accuracy in excess of 99 %. Leibniz Research Centre for Working Environment and Human Factors 2011-11-24 /pmc/articles/PMC5109015/ /pubmed/27857678 Text en Copyright © 2011 Nantasenamat et al. http://www.excli.de/documents/assignment_of_rights.pdf This is an Open Access article distributed under the following Assignment of Rights http://www.excli.de/documents/assignment_of_rights.pdf. You are free to copy, distribute and transmit the work, provided the original author and source are credited.
spellingShingle Original Article
Nantasenamat, Chanin
Preeyanon, Likit
Isarankura-Na-Ayudhya, Chartchalerm
Prachayasittikul, Virapong
PyBact: an algorithm for bacterial identification
title PyBact: an algorithm for bacterial identification
title_full PyBact: an algorithm for bacterial identification
title_fullStr PyBact: an algorithm for bacterial identification
title_full_unstemmed PyBact: an algorithm for bacterial identification
title_short PyBact: an algorithm for bacterial identification
title_sort pybact: an algorithm for bacterial identification
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109015/
https://www.ncbi.nlm.nih.gov/pubmed/27857678
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