Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection - Takahashi Y, Ueki M, Yamada M, Tamiya G, Motoike IN, Saigusa D, Sakurai M, Nagami F, Ogishima S, Koshiba S, Kinoshita K, Yamamoto M, Tomita H.

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...
Source: SafetyLit - Category: International Medicine & Public Health Tags: Ergonomics, Human Factors, Anthropometrics, Physiology Source Type: news