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

Pharmacogenomics. 2019 Sep;20(14):983-988. doi: 10.2217/pgs-2019-0119.

Authors

Arjun P Athreya¹, Ravishankar Iyer², Liewei Wang¹, Richard M Weinshilboum¹, William V Bobo³

Affiliations

¹ Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
² Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, IL 61820, USA.
³ Department of Psychiatry & Psychology, Mayo Clinic, Jacksonville, FL 32224, USA.

PMID: 31559920
DOI: 10.2217/pgs-2019-0119

No abstract available

Keywords: antidepressants; artificial intelligence; clinical response; depression; major depression.

Publication types

Editorial
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Antidepressive Agents / therapeutic use
Artificial Intelligence / trends*
Biomarkers / metabolism
Depressive Disorder / drug therapy*
Depressive Disorder / epidemiology
Depressive Disorder / genetics
Humans
Machine Learning*
Pharmacogenetics / trends*
Polymorphism, Single Nucleotide / genetics

Substances

Antidepressive Agents
Biomarkers

Grants and funding