Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response

Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatmen...

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Detalles Bibliográficos
Autores principales: Shumake, Jason, Mallard, Travis T., McGeary, John E., Beevers, Christopher G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881144/
https://www.ncbi.nlm.nih.gov/pubmed/33580158
http://dx.doi.org/10.1038/s41598-021-83338-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881144/
https://www.ncbi.nlm.nih.gov/pubmed/33580158
http://dx.doi.org/10.1038/s41598-021-83338-2

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