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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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Hauptverfasser: Cao, Han (Verfasst von) , Rajan, Sivanesan (Verfasst von) , Hahn, Bianka (Verfasst von) , Kocak, Ersoy (Verfasst von) , Brenner, Manuel (Verfasst von) , Hess, Florian (Verfasst von) , Schefzik, Roman (Verfasst von) , Durstewitz, Daniel (Verfasst von) , Koppe, Georgia (Verfasst von) , Schwarz, Emanuel (Verfasst von) , Schneider-Lindner, Verena (Verfasst von)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 17 April 2026
In: Scientific reports
Year: 2026, Jahrgang: 16, Pages: 1-10
ISSN:2045-2322
DOI:10.1038/s41598-026-43551-3
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-026-43551-3
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-026-43551-3
Volltext
Verfasserangaben:Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak, Manuel Brenner, Florian Hess, Roman Schefzik, Daniel Durstewitz, Georgia Koppe, Emanuel Schwarz & Verena Schneider-Lindner
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 21.05.2026
Beschreibung:Online Resource
ISSN:2045-2322
DOI:10.1038/s41598-026-43551-3

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