ArticlePLoS computational biology2020
Transfer learning enables prediction of CYP2D6 haplotype function.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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.
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Who cites it
25 citing papers in PubMed, 50 citations in OpenAlex.
- Homology-Based Variant-Effect Predictors Break Down on Cytochrome P450 Pharmacogenes.bioRxiv : the preprint server for biology · 2026Article
- Computational variant predictors for pharmacogenomics: from evaluation of single alleles to assessment of adverse drug reactions to antidepressants.The pharmacogenomics journal · 2026Article
- The evolving landscape of pharmacogenomics: Current achievements and future directions.Pharmacological reviews · 2026Review
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Validating the accuracy of mathematical model-based pharmacogenomics dose prediction with real-world data.European journal of clinical pharmacology · 2025Article
- Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance.British journal of clinical pharmacology · 2025Review
- Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.Cancers · 2024Review
- APF2: an improved ensemble method for pharmacogenomic variant effect prediction.The pharmacogenomics journal · 2024Article
- Targeted haplotyping in pharmacogenomics using Oxford Nanopore Technologies' adaptive sampling.Frontiers in pharmacology · 2023Article
- From gene to dose: Long-read sequencing and *-allele tools to refine phenotype predictions ofFrontiers in pharmacology · 2023Article
- Artificial intelligence in pharmacology research and practice.Clinical and translational science · 2023Review
- A paradigm shift in pharmacogenomics: From candidate polymorphisms to comprehensive sequencing.Basic & clinical pharmacology & toxicology · 2022Review
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
- From pharmacogenetics to pharmaco-omics: Milestones and future directions.HGG advances · 2022Review
- Keeping pace withPharmacogenomics · 2022Review
- Why We Need to Take a Closer Look at Genetic Contributions to CYP3A Activity.Frontiers in pharmacology · 2022Review
- ClinPharmSeq: A targeted sequencing panel for clinical pharmacogenetics implementation.PloS one · 2022Article
- Comprehensive Allele Genotyping in Critical Pharmacogenes Reduces Residual Clinical Risk in Diverse Populations.Clinical pharmacology and therapeutics · 2021Article
- Genomewide Association Studies in Pharmacogenomics.Clinical pharmacology and therapeutics · 2021Review
- Pharmacogenetics at Scale: An Analysis of the UK Biobank.Clinical pharmacology and therapeutics · 2021Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 1 country.
Funding
Abstract
Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in in vitro functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.
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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.