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However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for <jats:italic>Salmonella typhimurium<\/jats:italic>. We compared our predictions to the viability of experimental knock-outs of <jats:italic>S. typhimurium<\/jats:italic> and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-4-56","type":"journal-article","created":{"date-parts":[[2010,5,4]],"date-time":"2010-05-04T06:14:14Z","timestamp":1272953654000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":109,"title":["Identifying essential genes in bacterial metabolic networks with machine learning methods"],"prefix":"10.1186","volume":"4","author":[{"given":"Kitiporn","family":"Plaimas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Eils","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rainer","family":"K\u00f6nig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2010,5,3]]},"reference":[{"key":"445_CR1","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1038\/nrd892","volume":"1","author":"AL Hopkins","year":"2002","unstructured":"Hopkins AL, Groom CR: The druggable genome. 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