ArticleCell systems2018
Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.
Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 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
119 citing papers in PubMed, 252 citations in OpenAlex.
- Toward Advanced Quantitative Prediction of Missense Variant Effects.International journal of molecular sciences · 2026Article
- Prediction of human missense variant effects from functional evidence.Nature genetics · 2026Article
- VEFill: accurate and generalizable deep mutational scanning score imputation across protein domains.Molecular systems biology · 2026Article
- Massively parallel functional genomic assays in endocrinology: from promise to delivery.Journal of molecular endocrinology · 2026Review
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- Overcoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modeling.PLoS computational biology · 2026Article
- Multiplexed assays of variant effect for clinical variant interpretation.Nature reviews. Genetics · 2026Review
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score Distributions.bioRxiv : the preprint server for biology · 2025Article
- Overcoming Extrapolation Challenges of Deep Learning by Incorporating Physics in Protein Sequence-Function Modeling.bioRxiv : the preprint server for biology · 2025Article
- DeepProtein: deep learning library and benchmark for protein sequence learning.Bioinformatics (Oxford, England) · 2025Article
- Doblin: inferring dominant clonal lineages from high-resolution DNA barcoding time series.Bioinformatics (Oxford, England) · 2025Article
- VEFill: a model for accurate and generalizable deep mutational scanning score imputation across protein domains.bioRxiv : the preprint server for biology · 2025Article
- Classification of GBA1 variants and their impact on Parkinson's disease: an in silico score analysis.NPJ Parkinson's disease · 2025Article
- Multi-dimensional annotation of porcine variants using genomic and epigenomic features in pigs.BMC biology · 2025Article
- Locality-aware pooling enhances protein language model performance across varied applications.Bioinformatics (Oxford, England) · 2025Article
- Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability.eLife · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Guidelines for releasing a variant effect predictor.Genome biology · 2025Article
- High-throughput screening of human genetic variants by pooled prime editing.Cell genomics · 2025Article
59 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
5 authors at 2 institutions in 1 country.
Funding
Abstract
Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (https://envision.gs.washington.edu/).
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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.