Anti-Bacterial Agents; Drug Resistance, Bacterial; Machine Learning; Proteomics; Statistics and Probability; Biochemistry; Molecular Biology; Computer Science Applications; Computational Theory and Mathematics; Computational Mathematics
Abstract :
[en] [en] MOTIVATION: 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's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability.
RESULTS: 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.
AVAILABILITY AND IMPLEMENTATION: The code used to produce the results presented in this article is available at https://github.com/BorgwardtLab/MultimodalAMR.
Disciplines :
Pharmacy, pharmacology & toxicology
Author, co-author :
Visonà, Giovanni ; Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, Tübingen 72076, Germany
Duroux, Diane ; Université de Liège - ULiège > GIGA > GIGA Medical Genomics - Biostatistics, biomedicine and bioinformatics ; ETH AI Center, ETH Zürich, Andreasstrasse 5, Zürich 8092, Switzerland
Miranda, Lucas ; Research Group Statistical Genetics, Max Planck Institute of Psychiatry, Kraepelinstraße 10, München 80804, Germany
Sükei, Emese ; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés 28911, Spain
Li, Yiran; Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
Borgwardt, Karsten ; Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland ; Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland ; Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
Oliver, Carlos ; Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland ; Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland ; Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
Language :
English
Title :
Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information.
H2020 - 813533 - MLFPM2018 - Machine Learning Frontiers in Precision Medicine
Funders :
EU - European Union
Funding text :
This project has received funding from the European Union’s Framework Programme for Research and Innovation Horizon 2020 (2014–2020) under the Marie Skłodowska-Curie Grant Agreement No. 813533-MSCA-ITN-2018.
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