ReviewPharmacogenomics2018
Deep learning in pharmacogenomics: from gene regulation to patient stratification.
Review in Pharmacogenomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
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
63 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 2026Article
- Polysaccharide-functionalized gold, silver, and iron oxide nanoparticles for siRNA delivery: The role of artificial intelligence in design and optimization.Materials today. Bio · 2026Review
- Clinical Application of Pharmacogenomics in Stroke Management: Current Evidence and Future Directions.Journal of stroke · 2026Review
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- Machine-Learning-Aided Advanced Electrochemical Biosensors.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
- Transforming Pharmacogenomics and CRISPR Gene Editing with the Power of Artificial Intelligence for Precision Medicine.Pharmaceutics · 2025Review
- Regulatory QTLs affecting miRNA-mRNA interactions in cancer: mechanisms, methods, and clinical implications.Frontiers in molecular biosciences · 2025Review
- Review
- Epigenomics and the Brain-gut Axis: Impact of Adverse Childhood Experiences and Therapeutic Challenges.Journal of translational gastroenterology · 2024Article
- Revealing neural dynamical structure ofiScience · 2024Article
- Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations.ACS omega · 2024Review
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
- Pharmacogenomics and Big Data in medical oncology: developments and challenges.Therapeutic advances in medical oncology · 2024Review
- PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.Animals : an open access journal from MDPI · 2023Article
- The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.BMC oral health · 2023Article
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.Human genomics · 2023Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.
Indexed as
Identifiers
What Socratic 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 Socratic graph.