Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes

The accuracy of previous genetic studies in predicting polygenic psychiatric phenotypes has been limited mainly due to the limited power in distinguishing truly susceptible variants from null variants and the resulting overfitting. A novel prediction algorithm, Smooth-Threshold Multivariate Genetic...

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Detalles Bibliográficos
Autores principales: Takahashi, Yuta, Ueki, Masao, Tamiya, Gen, Ogishima, Soichi, Kinoshita, Kengo, Hozawa, Atsushi, Minegishi, Naoko, Nagami, Fuji, Fukumoto, Kentaro, Otsuka, Kotaro, Tanno, Kozo, Sakata, Kiyomi, Shimizu, Atsushi, Sasaki, Makoto, Sobue, Kenji, Kure, Shigeo, 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/PMC7442807/
https://www.ncbi.nlm.nih.gov/pubmed/32826857
http://dx.doi.org/10.1038/s41398-020-00957-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442807/
https://www.ncbi.nlm.nih.gov/pubmed/32826857
http://dx.doi.org/10.1038/s41398-020-00957-5

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