Publication: Pybact: An algorithm for bacterial identification
| dc.contributor.author | Chanin Nantasenamat | en_US |
| dc.contributor.author | Likit Preeyanon | en_US |
| dc.contributor.author | Chartchalerm Isarankura-Na-Ayudhya | en_US |
| dc.contributor.author | Virapong Prachayasittikul | en_US |
| dc.contributor.other | Mahidol University | en_US |
| dc.date.accessioned | 2018-05-03T07:55:06Z | |
| dc.date.available | 2018-05-03T07:55:06Z | |
| dc.date.issued | 2011-12-01 | en_US |
| dc.description.abstract | 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 %. | en_US |
| dc.identifier.citation | EXCLI Journal. Vol.10, (2011), 240-245 | en_US |
| dc.identifier.issn | 16112156 | en_US |
| dc.identifier.other | 2-s2.0-84855803086 | en_US |
| dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/123456789/11223 | |
| dc.rights | Mahidol University | en_US |
| dc.rights.holder | SCOPUS | en_US |
| dc.source.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84855803086&origin=inward | en_US |
| dc.subject | Agricultural and Biological Sciences | en_US |
| dc.subject | Biochemistry, Genetics and Molecular Biology | en_US |
| dc.subject | Pharmacology, Toxicology and Pharmaceutics | en_US |
| dc.title | Pybact: An algorithm for bacterial identification | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| mu.datasource.scopus | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84855803086&origin=inward | en_US |