Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information.

Details

Serval ID
serval:BIB_A4D8C2BA1377
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information.
Journal
Bioinformatics
Author(s)
Visonà G., Duroux D., Miranda L., Sükei E., Li Y., Borgwardt K., Oliver C.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
Published
Issued date
01/12/2023
Peer-reviewed
Oui
Volume
39
Number
12
Pages
btad717
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
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.
The code used to produce the results presented in this article is available at https://github.com/BorgwardtLab/MultimodalAMR.
Keywords
Proteomics, Anti-Bacterial Agents, Drug Resistance, Bacterial, Machine Learning
Pubmed
Open Access
Yes
Create date
12/01/2024 8:59
Last modification date
13/01/2024 7:09
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