{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:03:15Z","timestamp":1771657395281,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Research Council Discovery","award":["DP180102584"],"award-info":[{"award-number":["DP180102584"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pathogens"],"abstract":"<jats:p>Babesia infection of red blood cells can cause a severe disease called babesiosis in susceptible hosts. Bovine babesiosis causes global economic loss to the beef and dairy cattle industries, and canine babesiosis is considered a clinically significant disease. Potential therapeutic targets against bovine and canine babesiosis include members of the exportome, i.e., those proteins exported from the parasite into the host red blood cell. We developed three machine learning-derived methods (two novel and one adapted) to predict for every known Babesia bovis, Babesia bigemina, and Babesia canis protein the probability of being an exportome member. Two well-studied apicomplexan-related species, Plasmodium falciparum and Toxoplasma gondii, with extensive experimental evidence on their exportome or excreted\/secreted proteins were used as important benchmarks for the three methods. Based on 10-fold cross validation and multiple train\u2013validation\u2013test splits of training data, we expect that over 90% of the predicted probabilities accurately provide a secretory or non-secretory indicator. Only laboratory testing can verify that predicted high exportome membership probabilities are creditable exportome indicators. However, the presented methods at least provide those proteins most worthy of laboratory validation and will ultimately save time and money.<\/jats:p>","DOI":"10.3390\/pathogens10060660","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T11:07:02Z","timestamp":1622113622000},"page":"660","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis"],"prefix":"10.3390","volume":"10","author":[{"given":"Stephen J.","family":"Goodswen","sequence":"first","affiliation":[{"name":"School of Life Sciences, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7837-3171","authenticated-orcid":false,"given":"Paul J.","family":"Kennedy","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering and Information Technology, Australian Artificial Intelligence Institute, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7328-4831","authenticated-orcid":false,"given":"John T.","family":"Ellis","sequence":"additional","affiliation":[{"name":"School of Life Sciences, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1128\/CMR.13.3.451","article-title":"Babesiosis","volume":"13","author":"Homer","year":"2000","journal-title":"Clin. 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