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

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

Editorial

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 et al. Pharmacogenomics. 2019 Sep.

. 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.

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

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

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