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It is important to understand AMR\u2019s biological mechanisms for the development of new drugs and more rapid and accurate clinical diagnostics. The increasing availability of whole-genome SNP (single nucleotide polymorphism) information, obtained from whole-genome sequence data, along with AMR profiles provides an opportunity to use feature selection in machine learning to find AMR-associated mutations. This work describes the use of a supervised feature selection approach using deep neural networks to detect AMR-associated genetic factors from whole-genome SNP data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method, DNP-AAP (deep neural pursuit \u2013 average activation potential), was tested on a<jats:italic>Neisseria gonorrhoeae<\/jats:italic>dataset with paired whole-genome sequence data and resistance profiles to five commonly used antibiotics including penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime. The results show that DNP-AAP can effectively identify known AMR-associated genes in<jats:italic>N. gonorrhoeae<\/jats:italic>, and also provide a list of candidate genomic features (SNPs) that might lead to the discovery of novel AMR determinants. Logistic regression classifiers were built with the identified SNPs and the prediction AUCs (area under the curve) for penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime were 0.974, 0.969, 0.949, 0.994, and 0.976, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>DNP-AAP can effectively identify known AMR-associated genes in<jats:italic>N. gonorrhoeae<\/jats:italic>. It also provides a list of candidate genes and intergenic regions that might lead to novel AMR factor discovery. More generally, DNP-AAP can be applied to AMR analysis of any bacterial species with genomic variants and phenotype data. It can serve as a useful screening tool for microbiologists to generate genetic candidates for further lab experiments.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-019-3054-4","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T09:02:35Z","timestamp":1577178155000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection"],"prefix":"10.1186","volume":"20","author":[{"given":"Jinhong","family":"Shi","sequence":"first","affiliation":[]},{"given":"Yan","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Matthew G.","family":"Links","sequence":"additional","affiliation":[]},{"given":"Longhai","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jo-Anne R.","family":"Dillon","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Horsch","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9278-8859","authenticated-orcid":false,"given":"Anthony","family":"Kusalik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"3054_CR1","unstructured":"O\u2019Neill J. 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No human subjects, human material, or human data were involved in this research.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable. No details, images, or videos relating to an individual person are present in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"535"}}