A multi-task learning approach combining regression and classification tasks for joint feature selection
Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms, and has been widely applied in the biomedical analysis for shared biomarker identification. Although MTL has successfully supported either regression or classification tasks,...
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Article (Journal) |
| Langue: | anglais |
| Publié: |
17 April 2026
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| In: |
Scientific reports
Year: 2026, Volume: 16, Pages: 1-10 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-026-43551-3 |
| Accès en ligne: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-026-43551-3 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-026-43551-3 
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| Notes sur l'auteur: | Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak, Manuel Brenner, Florian Hess, Roman Schefzik, Daniel Durstewitz, Georgia Koppe, Emanuel Schwarz & Verena Schneider-Lindner |
| Résumé: | Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms, and has been widely applied in the biomedical analysis for shared biomarker identification. Although MTL has successfully supported either regression or classification tasks, incorporating mixed types of tasks into a unified MTL framework remains challenging, especially in biomedicine, where it can lead to biased biomarker identification. To address this issue, we propose an improved method of multi-task learning, MTLComb, which balances the weights of regression and classification tasks to promote unbiased biomarker identification. We demonstrate the algorithmic efficiency and clinical utility of MTLComb through analyses on both simulated data and actual biomedical studies pertaining to sepsis and schizophrenia. The code is available at https://github.com/transbioZI/MTLComb. |
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| Description: | Gesehen am 21.05.2026 |
| Description matérielle: | Online Resource |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-026-43551-3 |