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Article

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

Scientific reports - 12 Feb 2021

Shumake Jason, Mallard Travis T, McGeary John E, Beevers Christopher G

Abstract excerpt

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 Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 33580158
DOI: 10.1038/s41598-021-83338-2
PMCID: PMC7881144

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