Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: Can computational intelligence be used to augment clinical assessments?

Arjun P. Athreya; Ravishankar Iyer; Liewei Wang; Richard M. Weinshilboum; William V. Bobo

doi:10.2217/pgs-2019-0119

Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: Can computational intelligence be used to augment clinical assessments?

Arjun P. Athreya, Ravishankar Iyer, Liewei Wang, Richard M. Weinshilboum, William V. Bobo

Research output: Contribution to journal › Review article

Original language	English (US)
Pages (from-to)	983-988
Number of pages	6
Journal	Pharmacogenomics
Volume	20
Issue number	14
DOIs	https://doi.org/10.2217/pgs-2019-0119
State	Published - Jan 1 2019

Keywords

antidepressants
artificial intelligence
clinical response
depression
major depression

ASJC Scopus subject areas

Molecular Medicine
Genetics
Pharmacology

Access to Document

10.2217/pgs-2019-0119

Cite this

Athreya, A. P., Iyer, R., Wang, L., Weinshilboum, R. M., & Bobo, W. V. (2019). Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: Can computational intelligence be used to augment clinical assessments? Pharmacogenomics, 20(14), 983-988. https://doi.org/10.2217/pgs-2019-0119

Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment : Can computational intelligence be used to augment clinical assessments? / Athreya, Arjun P.; Iyer, Ravishankar; Wang, Liewei; Weinshilboum, Richard M.; Bobo, William V.

In: Pharmacogenomics, Vol. 20, No. 14, 01.01.2019, p. 983-988.

Research output: Contribution to journal › Review article

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