In:
Current Neuropharmacology, Bentham Science Publishers Ltd., Vol. 21, No. 12 ( 2023-12), p. 2395-2408
Abstract:
Traditional medicine and biomedical sciences are reaching a turning point because of the
constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another
promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical
strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up
the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with
mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which
first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective
and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they
will gain a unique role in the next future in improving personalized treatments in psychiatry.
Type of Medium:
Online Resource
ISSN:
1570-159X
DOI:
10.2174/1570159X21666230808170123
Language:
English
Publisher:
Bentham Science Publishers Ltd.
Publication Date:
2023
detail.hit.zdb_id:
2119376-9
