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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?

作者信息

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

机构信息

Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.

Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, IL 61820, USA.

出版信息

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

DOI:10.2217/pgs-2019-0119

PMID:31559920

Abstract

摘要

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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?

作者信息

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

机构信息

Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.

Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, IL 61820, USA.

出版信息

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

DOI:10.2217/pgs-2019-0119

PMID:31559920

Abstract

摘要

相似文献

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.

Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder.

Expert Rev Clin Pharmacol. 2022 Aug;15(8):927-944. doi: 10.1080/17512433.2022.2112949. Epub 2022 Aug 21.

The pharmacogenetics of depression: enter the GWAS.

Am J Psychiatry. 2010 May;167(5):493-5. doi: 10.1176/appi.ajp.2010.10020244.

Pharmacogenomic strategy for individualizing antidepressant therapy.

Dialogues Clin Neurosci. 2008;10(4):401-8. doi: 10.31887/DCNS.2008.10.4/kmlin.

The future of antidepressant pharmacogenetics is arriving. Introduction.

Prog Neuropsychopharmacol Biol Psychiatry. 2005 Jul;29(6):996-8. doi: 10.1016/j.pnpbp.2005.03.016.

Pharmacogenomics of antidepressant drugs.

Pharmacol Ther. 2009 Oct;124(1):57-73. doi: 10.1016/j.pharmthera.2009.06.007. Epub 2009 Jun 27.

Pharmacogenetics of antidepressant drugs: current clinical practice and future directions.

Pharmacogenomics. 2012 Mar;13(4):441-64. doi: 10.2217/pgs.12.1.

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.

J Affect Disord. 2018 Dec 1;241:519-532. doi: 10.1016/j.jad.2018.08.073. Epub 2018 Aug 14.

Current therapy and future treatment strategies for depression (Paris, January 2003).

J Psychopharmacol. 2003 Sep;17(3):337-41. doi: 10.1177/02698811030173017.

The influence of ethnicity and antidepressant pharmacogenetics in the treatment of depression.

Drug Metabol Drug Interact. 2000;16(1):39-67. doi: 10.1515/dmdi.2000.16.1.39.

引用本文的文献

Artificial intelligence, medications, pharmacogenomics, and ethics.

Pharmacogenomics. 2024;25(14-15):611-622. doi: 10.1080/14622416.2024.2428587. Epub 2024 Nov 15.

Pharmacogenomic augmented machine learning in electronic health record alerts: A health system-wide usability survey of clinicians.

Clin Transl Sci. 2024 Oct;17(10):e70044. doi: 10.1111/cts.70044.

Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

Bipolar Disord. 2025 Feb;27(1):36-46. doi: 10.1111/bdi.13506. Epub 2024 Oct 3.

Cross-EHR validation of antidepressant response algorithm and links with genetics of psychiatric traits.

medRxiv. 2024 Sep 12:2024.09.11.24313478. doi: 10.1101/2024.09.11.24313478.

Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.

Hum Genomics. 2023 Jul 14;17(1):62. doi: 10.1186/s40246-023-00508-1.

Machine learning in biosignals processing for mental health: A narrative review.

Front Psychol. 2023 Jan 13;13:1066317. doi: 10.3389/fpsyg.2022.1066317. eCollection 2022.

Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review.

J Med Internet Res. 2022 Mar 14;24(3):e33560. doi: 10.2196/33560.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments.

Clin Psychopharmacol Neurosci. 2021 Nov 30;19(4):577-588. doi: 10.9758/cpn.2021.19.4.577.

Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication.

Transl Psychiatry. 2021 Oct 7;11(1):513. doi: 10.1038/s41398-021-01632-z.

Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction.

Front Pharmacol. 2021 Jan 11;11:614048. doi: 10.3389/fphar.2020.614048. eCollection 2020.