{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T17:21:29Z","timestamp":1789924889851,"version":"4.0.1"},"reference-count":48,"publisher":"Microbiology Society","issue":"12","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,1]]},"abstract":"<jats:p>\n                    With the ever-expanding number of available sequences from bacterial genomes, and the expectation that this data type will be the primary one generated from both diagnostic and research laboratories for the foreseeable future, then there is both an opportunity and a need to evaluate how effectively computational approaches can be used within bacterial genomics to predict and understand complex phenotypes, such as pathogenic potential and host source. This article applied various quantitative methods such as diversity indexes, pangenome-wide association studies (GWAS) and dimensionality reduction techniques to better understand the data and then compared how well unsupervised and supervised machine learning (ML) methods could predict the source host of the isolates. The study uses the example of the pangenomes of 1203\n                    <jats:italic>\n                      <jats:named-content content-type=\"species\">\n                        <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/doi.org\/10.1601\/nm.11017\" xlink:type=\"simple\">Salmonella enterica<\/jats:ext-link>\n                      <\/jats:named-content>\n                    <\/jats:italic>\n                    serovar Typhimurium isolates in order to predict 'host of isolation' using these different methods. The article is aimed as a review of recent applications of ML in infection biology, but also, by working through this specific dataset, it allows discussion of the advantages and drawbacks of the different techniques. As with all such sub-population studies, the biological relevance will be dependent on the quality and diversity of the input data. Given this major caveat, we show that supervised ML has the potential to add real value to interpretation of bacterial genomic data, as it can provide probabilistic outcomes for important phenotypes, something that is very difficult to achieve with the other methods.\n                  <\/jats:p>","DOI":"10.1099\/mgen.0.000317","type":"journal-article","created":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T12:12:28Z","timestamp":1574943148000},"source":"Crossref","is-referenced-by-count":40,"title":["A guide to machine learning for bacterial host attribution using genome sequence data"],"prefix":"10.1099","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4850-8423","authenticated-orcid":false,"given":"Nadejda","family":"Lupolova","sequence":"first","affiliation":[{"name":"Division of Infection and Immunity, The Roslin Institute, University of Edinburgh, Easter Bush Campus, Edinburgh, EH25 9RG, 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