Advancing translational research in neuroscience through multi-task learning

• Verfasst von: Cao, Han [VerfasserIn]
• Verfasst von: Hong, Xudong [VerfasserIn]
• Verfasst von: Tost, Heike [VerfasserIn]
• Verfasst von: Meyer-Lindenberg, Andreas [VerfasserIn]
• Verfasst von: Schwarz, Emanuel [VerfasserIn]
• Titel: Advancing translational research in neuroscience through multi-task learning
• Verf.angabe: Han Cao, Xudong Hong, Heike Tost, Andreas Meyer-Lindenberg and Emanuel Schwarz
• E-Jahr: 2022
• Jahr: 17 November 2022
• Umfang: 13 S.
• Fussnoten: Gesehen am 18.01.2023
• Titel Quelle: Enthalten in: Frontiers in psychiatry
• Ort Quelle: Lausanne : Frontiers Research Foundation, 2007
• Jahr Quelle: 2022
• Band/Heft Quelle: 13(2022), Artikel-ID 993289, Seite 1-13
• ISSN Quelle: 1664-0640
• DOI: doi:10.3389/fpsyt.2022.993289
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1831338017

Abstract

Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms. Recent advances in machine learning have the potential to substantially advance such translational research through the simultaneous analysis of different data modalities. This review focuses on one of such approaches, the so-called “multi-task learning” (MTL), and describes its potential utility for multi-modal data analyses in neuroscience. We summarize the methodological development of MTL starting from conventional machine learning, and present several scenarios that appear particularly suitable for its application. For these scenarios, we highlight different types of MTL algorithms, discuss emerging technological adaptations, and provide a step-by-step guide for readers to apply the MTL approach in their own studies. With its ability to simultaneously analyze multiple data modalities, MTL may become an important element of the analytics repertoire used in future neuroscience research and beyond.