ReviewDisease models & mechanisms2022
Interpreting protein variant effects with computational predictors and deep mutational scanning.
Review in Disease models & mechanisms, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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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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.
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Who cites it
43 citing papers in PubMed.
- Bridging structure and function: artificial intelligence-based modelling of kidney proteins.Nature reviews. Nephrology · 2026Review
- Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling.BMC bioinformatics · 2026Article
- Computational variant predictors for pharmacogenomics: from evaluation of single alleles to assessment of adverse drug reactions to antidepressants.The pharmacogenomics journal · 2026Article
- Creating an atlas of variant effects to resolve variants of uncertain significance and guide cardiovascular medicine.Nature reviews. Cardiology · 2026Review
- RNA-guided clarity: The potential for resolving variant uncertainty in clinical exome sequencing.Genetics in medicine open · 2026Article
- Reclassification of missense variant pathogenicity using ClinGen recommendations for recalibrated PP3/BP4 in silico predictor score thresholds.Genetics in medicine open · 2026Article
- A small molecule stabilizer rescues the surface expression of nearly all missense variants in a GPCR.Nature structural & molecular biology · 2025Article
- Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation.bioRxiv : the preprint server for biology · 2025Article
- Assessing the performance of 28 pathogenicity prediction methods on rare single nucleotide variants in coding regions.BMC genomics · 2025Article
- PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.Human genetics · 2025Article
- Massive mutagenesis reveals an incomplete amyloid motif in Bri2 that turns amyloidogenic upon C-terminal extension.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Guidelines for releasing a variant effect predictor.Genome biology · 2025Article
- QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.Human genetics · 2025Article
- Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance.British journal of clinical pharmacology · 2025Review
- Missense variant analysis in the TRPV1 ARD reveals the unexpected functional significance of a methionine.PloS one · 2025Article
- Benchmarking of variant pathogenicity prediction methods using a population genetics approach.Bioinformatics advances · 2025Article
- Complementary Roles of Structure and Variant Effect Predictors in RyR1 Clinical Interpretation.Human mutation · 2025Article
- Structural and functional prediction, evaluation, and validation in the post-sequencing era.Computational and structural biotechnology journal · 2024Review
- Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes.Cell reports · 2024Article
- Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.
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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 Socratic graph.