{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:06:14Z","timestamp":1784333174544,"version":"3.55.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T00:00:00Z","timestamp":1700784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Framework Programme for Research and Innovation Horizon 2020"},{"name":"Marie Sk\u0142odowska-Curie","award":["813533-MSCA-ITN-2018"],"award-info":[{"award-number":["813533-MSCA-ITN-2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen\u2019s resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction. By adopting this multi-view representation of the pathogenic samples and leveraging the scale of the available datasets, our models outperformed the previous single-drug and single-species predictive models by statistically significant margins. We extensively validated the multi-drug setting, highlighting the challenges in generalizing beyond the training data distribution, and quantitatively demonstrate how suitable representations of antimicrobial drugs constitute a crucial tool in the development of clinically relevant predictive models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code used to produce the results presented in this article is available at https:\/\/github.com\/BorgwardtLab\/MultimodalAMR.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad717","type":"journal-article","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T03:19:57Z","timestamp":1700882397000},"source":"Crossref","is-referenced-by-count":23,"title":["Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-5448","authenticated-orcid":false,"given":"Giovanni","family":"Vison\u00e0","sequence":"first","affiliation":[{"name":"Department of Empirical Inference, Max Planck Institute for Intelligent Systems , Max-Planck-Ring 4 , T\u00fcbingen 72076, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8564-3157","authenticated-orcid":false,"given":"Diane","family":"Duroux","sequence":"additional","affiliation":[{"name":"BIO3\u2014GIGA-R Medical Genomics, University of Li\u00e8ge , Avenue de l\u2019H\u00f4pital 11 , Li\u00e8ge 4000, Belgium"},{"name":"ETH AI Center, ETH Z\u00fcrich , Andreasstrasse 5 , Z\u00fcrich 8092, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5484-6744","authenticated-orcid":false,"given":"Lucas","family":"Miranda","sequence":"additional","affiliation":[{"name":"Research Group Statistical Genetics, Max Planck Institute of Psychiatry , Kraepelinstra\u00dfe 10 , M\u00fcnchen 80804, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8626-7026","authenticated-orcid":false,"given":"Emese","family":"S\u00fckei","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, Universidad Carlos III de Madrid , Legan\u00e9s 28911, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Z\u00fcrich , Basel 4058, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7221-2393","authenticated-orcid":false,"given":"Karsten","family":"Borgwardt","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Z\u00fcrich , Basel 4058, Switzerland"},{"name":"Swiss Institute for Bioinformatics (SIB) , Amphip\u00f4le, Quartier UNIL-Sorge , Lausanne 1015, Switzerland"},{"name":"Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry , Martinsried 82152, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-8795","authenticated-orcid":false,"given":"Carlos","family":"Oliver","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Z\u00fcrich , Basel 4058, Switzerland"},{"name":"Swiss Institute for Bioinformatics (SIB) , Amphip\u00f4le, Quartier UNIL-Sorge , Lausanne 1015, 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