ArticleAmerican journal of human genetics2018
ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants.
Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 162 papers.
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162 citing papers in PubMed.
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- Prediction of human missense variant effects from functional evidence.Nature genetics · 2026Article
- TTC14 dysfunction contributing to microcephaly and lissencephaly spectrum features through protein mislocalization and impaired RNA processing.Human genetics · 2026Article
- An integrated, scaled approach to resolve TSC2 variants of uncertain significance.Nature communications · 2026Article
- Defining the molecular tolerance-to-damage landscape of SMARCA4 helicase genetic alterations.bioRxiv : the preprint server for biology · 2026Article
- Domain-Specific Computational, Functional and Structural Methods Enable Interpretation ofCurrent oncology (Toronto, Ont.) · 2026Article
- Systematic mapping of O-GlcNAc transferase and O-GlcNAcase defines disease-associated variants.The Journal of biological chemistry · 2026Article
- Semantic embedding of variant effect annotations enables rapid and accurate pathogenicity prediction with VUS.Life.Scientific reports · 2026Article
- Article
- MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.Scientific reports · 2026Article
- Low-penetrance TP53 variants are mainly hypomorphic: an underestimated issue with high clinical significance.NPJ genomic medicine · 2026Article
- Gene-specific pathogenicity predictor for chromatin remodeling BAF complex-associated neurodevelopmental disorders.HGG advances · 2026Article
- Missense variants in DPYSL5 associated with neurodevelopmental disorders and brain malformations cause impaired neuronal maturation in vitro.Molecular psychiatry · 2026Article
- Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes.BioData mining · 2026Article
- EMMVEP: An Ensemble Method for Protein Missense Variant Effect Prediction Based on Multi-Source Feature Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- MissenseHMM: state-based annotations for missense variants through joint modeling of pathogenicity scores.bioRxiv : the preprint server for biology · 2026Article
- Tutorial for variant interrogation in tumor samples.PLoS computational biology · 2026Article
- Enhancing missense variant classification in predicted intrinsically disordered regions.PloS one · 2026Article
- Comprehensive analysis of non-synonymous single-nucleotide polymorphism of human TSC1 and TSC2 genes: An in silico approach.PloS one · 2026Article
- Structural Genomics Defines PBRM1 Bromodomain Variant Function in ccRCC.Human mutation · 2026Article
102 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Advances in high-throughput DNA sequencing have revolutionized the discovery of variants in the human genome; however, interpreting the phenotypic effects of those variants is still a challenge. While several computational approaches to predict variant impact are available, their accuracy is limited and further improvement is needed. Here, we introduce ClinPred, an efficient tool for identifying disease-relevant nonsynonymous variants. Our predictor incorporates two machine learning algorithms that use existing pathogenicity scores and, notably, benefits from inclusion of normal population allele frequency from the gnomAD database as an input feature. Another major strength of our approach is the use of ClinVar-a rapidly growing database that allows selection of confidently annotated disease-causing variants-as a training set. Compared to other methods, ClinPred showed superior accuracy for predicting pathogenicity, achieving the highest area under the curve (AUC) score and increasing both the specificity and sensitivity in different test datasets. It also obtained the best performance according to various other metrics. Moreover, ClinPred performance remained robust with respect to disease type (cancer or rare disease) and mechanism (gain or loss of function). Importantly, we observed that adding allele frequency as a predictive feature-as opposed to setting fixed allele frequency cutoffs-boosts the performance of prediction. We provide pre-computed ClinPred scores for all possible human missense variants in the exome to facilitate its use by the community.
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