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

Visonà, Giovanni; Duroux, Diane; Miranda, Lucas; Sükei, Emese; Li, Yiran; Borgwardt, K; Oliver, Carlos

doi:10.1093/bioinformatics/btad717

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

Visonà, G., Duroux, D., Miranda, L., Sükei, E., Li, Y., Borgwardt, K., et al. (2023). Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information. Bioinformatics, btad717. doi:10.1093/bioinformatics/btad717.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000E-00ED-7 Version Permalink: https://hdl.handle.net/21.11116/0000-000E-00EE-6

Genre: Journal Article

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Creators:
Visonà, Giovanni¹, Author
Duroux, Diane¹, Author
Miranda, Lucas¹, Author
Sükei, Emese¹, Author
Li, Yiran¹, Author
Borgwardt, K², Author
Oliver, Carlos², Author

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1External Organizations, ou_persistent22
2Borgwardt, Karsten / Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Max Planck Society, ou_3502542

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Dates: Published Online: 2023-11-24

Publication Status: Published online

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Identifiers: DOI: 10.1093/bioinformatics/btad717

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Title: Bioinformatics

Source Genre: Journal

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Pages: - Volume / Issue: - Sequence Number: btad717 Start / End Page: - Identifier: ISSN: 1367-4803
ISSN: 1367-4811