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

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 (...

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Vydáno v:Transl Psychiatry
Hlavní autoři: Takahashi, Yuta, Ueki, Masao, Yamada, Makoto, Tamiya, Gen, Motoike, Ikuko N., Saigusa, Daisuke, Sakurai, Miyuki, Nagami, Fuji, Ogishima, Soichi, Koshiba, Seizo, Kinoshita, Kengo, Yamamoto, Masayuki, Tomita, Hiroaki
Médium: Artigo
Jazyk:Inglês
Vydáno: Nature Publishing Group UK 2020
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On-line přístup:https://ncbi.nlm.nih.gov/pmc/articles/PMC7237664/
https://ncbi.nlm.nih.gov/pubmed/32427830
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41398-020-0831-9
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