Machine Learning: An Overview and Applications in Pharmacogenetics

Abstract

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.

A systematic literature review for the prediction of anticancer drug response using various machine‐learning and deep‐learning techniques

“…The year‐wise distribution of the total selected papers can be seen in the graph below in Figure 2, which mainly emphasizes predicting the anticancer drug response (Adam et al, 2020; Ahmadi Moughari & Eslahchi, 2020; Choi et al, 2020; Koch et al, 2020; Kong et al, 2020; Kurilov et al, 2020; Patel et al, 2020; Sharma & Rani, 2020a; Wang, Li, Carpenter, & Guan, 2020; Zhu et al, 2020). The years between 2020 and 2021 (Cilluffo et al, 2021; Feng et al, 2021; Franco et al, 2021; Gerdes et al, 2021; Kim et al, 2021; Lv et al, 2021; Mudali et al, 2020; Nguyen et al, 2021; Partin et al, 2021; Patel & Shah, 2021; Piroozmand et al, 2020; Rafique et al, 2021; Schperberg et al, 2020; Vatansever et al, 2021; Vougas et al, 2020; Wang, Li, & Guan, 2020; Yu et al, 2021; Zhang et al, 2021) recorded the maximum number of publications up to 21–28 articles, while 2013 had no publications in the selected criteria.…”

Supporting Machine Learning Model in the Treatment of Chronic Pain

“…Furthermore, questionnaires were offered to patients to monitor symptomatic improvement in pain and quality of life during the treatment period. One of the pain-rating scales used in epidemiologic and clinical research to measure the severity or frequency of different symptoms is the Visual Analogue Scale (VAS) [30]. The pain VAS, which ranges from "no pain" (0 value) to "worst pain" (10 value), is a unidimensional measure of pain intensity that is used to track patients' pain progression or compare the level of pain in patients with similar diseases.…”

Genetic Association Study and Machine Learning to Investigate Differences in Platelet Reactivity in Patients with Acute Ischemic Stroke Treated with Aspirin

“…Moreover, the interaction of genetic polymorphisms as well as clinical factors may influence sensitivity to aspirin [ 40 ]. Over the past several years, machine learning (ML) models have been proven to be able to solve various problems in the medical and biological fields, including pharmacogenetics [ 41 , 42 ]. One of the key advantages of the ML approaches lies in their ability to find unobvious relationships and make inferences from the complex data.…”