Comparative evaluation of machine learning strategies for analyzing big data in psychiatry

• Verfasst von: Cao, Han [VerfasserIn]
• Verfasst von: Meyer-Lindenberg, Andreas [VerfasserIn]
• Verfasst von: Schwarz, Emanuel [VerfasserIn]
• Titel: Comparative evaluation of machine learning strategies for analyzing big data in psychiatry
• Verf.angabe: Han Cao, Andreas Meyer-Lindenberg, Emanuel Schwarz (Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University)
• E-Jahr: 2018
• Jahr: 29 October 2018
• Umfang: 15 S.
• Fussnoten: Gesehen am 15.03.2019
• Titel Quelle: Enthalten in: International journal of molecular sciences
• Ort Quelle: Basel : Molecular Diversity Preservation International, 2000
• Jahr Quelle: 2018
• Band/Heft Quelle: Volume 19, issue 11 (2018) Artikel-Nummer 3387, Seite 1-15
• ISSN Quelle: 1422-0067
• ISSN Quelle: 1661-6596
• DOI: doi:10.3390/ijms19113387
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: biomarker discovery
• Sach-SW: machine learning
• Sach-SW: multi-task learning
• Sach-SW: psychiatry
• K10plus-PPN: 1590359275

Abstract

The requirement of innovative big data analytics has become a critical success factor for research in biological psychiatry. Integrative analyses across distributed data resources are considered essential for untangling the biological complexity of mental illnesses. However, little is known about algorithm properties for such integrative machine learning. Here, we performed a comparative analysis of eight machine learning algorithms for identification of reproducible biological fingerprints across data sources, using five transcriptome-wide expression datasets of schizophrenia patients and controls as a use case. We found that multi-task learning (MTL) with network structure (MTL_NET) showed superior accuracy compared to other MTL formulations as well as single task learning, and tied performance with support vector machines (SVM). Compared to SVM, MTL_NET showed significant benefits regarding the variability of accuracy estimates, as well as its robustness to cross-dataset and sampling variability. These results support the utility of this algorithm as a flexible tool for integrative machine learning in psychiatry.