Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection.

ABSTRACT: To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Cente

SUBMITTER: Takahashi Y 

PROVIDER: S-EPMC7237664 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature