Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy.
Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, Pediatric Division, University of Verona, 37134 Verona, Italy.
Genes (Basel). 2021 Sep 26;12(10):1511. doi: 10.3390/genes12101511.
This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.
这篇叙述性综述旨在提供过去 10 年来机器学习（ML）技术及其在药物遗传学（如抗抑郁药、抗癌药和华法林药物）中的主要应用的概述。ML 涉及研究、设计和开发算法的工作，这些算法使计算机能够在无需显式编程的情况下学习。ML 是人工智能的一个分支，迄今为止，它在生物医学的广泛任务中表现出了令人满意的性能。根据最终目标，ML 可以定义为监督式（SML）或无监督式（UML）。当预测是研究的重点时，会应用 SML 技术。另一方面，当结果未知时，会使用 UML 技术，而研究的目标是揭示数据的潜在结构。越来越多地使用复杂的 ML 算法可能有助于提高药物遗传学的知识水平。
