{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:01:57Z","timestamp":1768003317867,"version":"3.49.0"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chem-Bio Diagnostics program","award":["HDTRA1-12-D-0003-0023"],"award-info":[{"award-number":["HDTRA1-12-D-0003-0023"]}]},{"DOI":"10.13039\/100000774","name":"Defense Threat Reduction Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000774","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral\/bacterial\/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse-engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes and Inflammatory\/Innate Response.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>Data and code are available on the Gene Expression Omnibus and github.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab089","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T06:07:18Z","timestamp":1612418838000},"page":"2347-2355","source":"Crossref","is-referenced-by-count":5,"title":["An OMICs-based meta-analysis to support infection state stratification"],"prefix":"10.1093","volume":"37","author":[{"given":"Ashleigh C","family":"Myall","sequence":"first","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool , Liverpool L697ZB, UK"},{"name":"Department of Mathematics, Imperial College London , London SW7 2AZ, UK"}]},{"given":"Simon","family":"Perkins","sequence":"additional","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool , Liverpool L697ZB, UK"}]},{"given":"David","family":"Rushton","sequence":"additional","affiliation":[{"name":"Defence and Security Analysis Division, Defence Science and Technology laboratory (DSTL) , Salisbury SP4 0JQ, UK"}]},{"given":"Jonathan","family":"David","sequence":"additional","affiliation":[{"name":"Chemical, Biological and Radiological Division, Defence Science and Technology laboratory (DSTL) , Salisbury SP4 0JQ, UK"}]},{"given":"Phillippa","family":"Spencer","sequence":"additional","affiliation":[{"name":"Cyber and Information Systems Division, Defence Science and Technology laboratory (DSTL) , Salisbury SP4 0JQ, UK"}]},{"given":"Andrew R","family":"Jones","sequence":"additional","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool , Liverpool L697ZB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9600-7757","authenticated-orcid":false,"given":"Philipp","family":"Antczak","sequence":"additional","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool , Liverpool L697ZB, UK"},{"name":"Center for Molecular Medicine Cologne, University of Cologne , Cologne 50931, Germany"}]}],"member":"286","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"2023051609081360200_btab089-B1","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1038\/ng0707-807","article-title":"On the design and analysis of gene expression studies in human populations","volume":"39","author":"Akey","year":"2007","journal-title":"Nat. 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