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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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237664/
https://www.ncbi.nlm.nih.gov/pubmed/32427830
http://dx.doi.org/10.1038/s41398-020-0831-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237664/
https://www.ncbi.nlm.nih.gov/pubmed/32427830
http://dx.doi.org/10.1038/s41398-020-0831-9

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