ReviewGenes2021
Machine Learning: An Overview and Applications in Pharmacogenetics.
Review in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Leveraging Machine Learning to Predict Warfarin Sensitivity in the Puerto Rican Population: A Pharmacogenomic Approach.International journal of environmental research and public health · 2026Article
- A Perspective on Artificial Intelligence for Molecular Pathologists.The Journal of molecular diagnostics : JMD · 2025Review
- The Role of Vitamin D Metabolism Genes and Their Genomic Background in Shaping Cyclosporine A Dosage Parameters after Kidney Transplantation.Journal of clinical medicine · 2024Article
- The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.Functional & integrative genomics · 2024Review
- Pharmacogenomics: Challenges and Future.Genes · 2024Article
- The polygenic implication of clopidogrel responsiveness: Insights from platelet reactivity analysis and next-generation sequencing.PloS one · 2024Article
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
- Artificial Intelligence Applications in the Diagnosis of Neuromuscular Diseases: A Narrative Review.Cureus · 2023Review
- Supporting Machine Learning Model in the Treatment of Chronic Pain.Biomedicines · 2023Article
- Genetic Association Study and Machine Learning to Investigate Differences in Platelet Reactivity in Patients with Acute Ischemic Stroke Treated with Aspirin.Biomedicines · 2022Article
- Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms.Life (Basel, Switzerland) · 2022Article
- Review
- Integrated bioinformatical analysis, machine learning andFrontiers in pharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.