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 › peer-review

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 - 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

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 et al.
In: Pharmacogenomics, Vol. 20, No. 14, 2019, p. 983-988.

Research output: Contribution to journal › Review article › peer-review

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

keywords = "antidepressants, artificial intelligence, clinical response, depression, major depression",

author = "Athreya, {Arjun P.} and Ravishankar Iyer and Liewei Wang and Weinshilboum, {Richard M.} and Bobo, {William V.}",

note = "Funding Information: L Wang and RM Weinshilboum are co-founders and stockholders in OneOme LLC. WV Bobo's research has been supported by the National Institute of Mental Health, the Agency for Healthcare Research & Quality, and the Mayo Foundation for Medical Education and Research. He has contributed chapters to UpToDate concerning the use of antidepressants and atypical antipsychotic drugs for treating adults with bipolar major depression. All others declared no competing interests for this work. This material is based upon work partially supported by a Mayo Clinic and Illinois Alliance Fellowship for Technology-Based Healthcare Research; a CompGen Fellowship; an IBM Faculty Award; the National Science Foundation (NSF) under Grant Number CNS 13-37732; the National Institutes of Health (NIH) under Grant Numbers U19 GM61388, R01 GM28157, RC2 GM092729, R24 GM078233, RC2 GM092729 and T32 GM072474; and the Mayo Clinic Center for Individualized Medicine. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or the NIH. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Funding Information: L Wang and RM Weinshilboum are co-founders and stockholders in OneOme LLC. WV Bobo{\textquoteright}s research has been supported by the National Institute of Mental Health, the Agency for Healthcare Research & Quality, and the Mayo Foundation for Medical Education and Research. He has contributed chapters to UpToDate concerning the use of antidepressants and atypical antipsychotic drugs for treating adults with bipolar major depression. All others declared no competing interests for this work. This material is based upon work partially supported by a Mayo Clinic and Illinois Alliance Fellowship for Technology-Based Healthcare Research; a CompGen Fellowship; an IBM Faculty Award; the National Science Foundation (NSF) under Grant Number CNS 13-37732; the National Institutes of Health (NIH) under Grant Numbers U19 GM61388, R01 GM28157, RC2 GM092729, R24 GM078233, RC2 GM092729 and T32 GM072474; and the Mayo Clinic Center for Individualized Medicine. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or the NIH. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.",

year = "2019",

doi = "10.2217/pgs-2019-0119",

language = "English (US)",

volume = "20",

pages = "983--988",

journal = "Pharmacogenomics",

issn = "1462-2416",

publisher = "Future Medicine Ltd.",

number = "14",

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TY - JOUR

T1 - Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment

T2 - Can computational intelligence be used to augment clinical assessments?

AU - Athreya, Arjun P.

AU - Iyer, Ravishankar

AU - Wang, Liewei

AU - Weinshilboum, Richard M.

AU - Bobo, William V.

N1 - Funding Information: L Wang and RM Weinshilboum are co-founders and stockholders in OneOme LLC. WV Bobo's research has been supported by the National Institute of Mental Health, the Agency for Healthcare Research & Quality, and the Mayo Foundation for Medical Education and Research. He has contributed chapters to UpToDate concerning the use of antidepressants and atypical antipsychotic drugs for treating adults with bipolar major depression. All others declared no competing interests for this work. This material is based upon work partially supported by a Mayo Clinic and Illinois Alliance Fellowship for Technology-Based Healthcare Research; a CompGen Fellowship; an IBM Faculty Award; the National Science Foundation (NSF) under Grant Number CNS 13-37732; the National Institutes of Health (NIH) under Grant Numbers U19 GM61388, R01 GM28157, RC2 GM092729, R24 GM078233, RC2 GM092729 and T32 GM072474; and the Mayo Clinic Center for Individualized Medicine. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or the NIH. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Funding Information: L Wang and RM Weinshilboum are co-founders and stockholders in OneOme LLC. WV Bobo’s research has been supported by the National Institute of Mental Health, the Agency for Healthcare Research & Quality, and the Mayo Foundation for Medical Education and Research. He has contributed chapters to UpToDate concerning the use of antidepressants and atypical antipsychotic drugs for treating adults with bipolar major depression. All others declared no competing interests for this work. This material is based upon work partially supported by a Mayo Clinic and Illinois Alliance Fellowship for Technology-Based Healthcare Research; a CompGen Fellowship; an IBM Faculty Award; the National Science Foundation (NSF) under Grant Number CNS 13-37732; the National Institutes of Health (NIH) under Grant Numbers U19 GM61388, R01 GM28157, RC2 GM092729, R24 GM078233, RC2 GM092729 and T32 GM072474; and the Mayo Clinic Center for Individualized Medicine. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or the NIH. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

PY - 2019

Y1 - 2019

KW - antidepressants

KW - artificial intelligence

KW - clinical response

KW - depression

KW - major depression

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VL - 20

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JO - Pharmacogenomics

JF - Pharmacogenomics

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ER -

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

Keywords

ASJC Scopus subject areas

Access to Document

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