ArticleInternational journal of molecular sciences2022
A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- A structure-based tool to interpret the significance of kinase mutations in clinical next generation sequencing in cancer.Frontiers in oncology · 2025Article
- Benchmarking of variant pathogenicity prediction methods using a population genetics approach.Bioinformatics advances · 2025Article
- Analysis of damaging non-synonymous SNPs in GPx1 gene associated with the progression of diverse cancers through a comprehensive in silico approach.Scientific reports · 2024Article
- Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes.Scientific reports · 2024Article
- Article
- Comment to the article by Pedro Brotons: Validation and implementation of a direct RT-qPCR method for rapid screening of SARS-CoV-2 infection by using non-invasive saliva samples, IJID 110 (2021) 363-370.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including
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