Article

Predicting functional consequences of mutations using molecular interaction network features

2021-03-08

Abstract excerpt

Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological functio...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: cb097e81-7868-56f6-8279-1f0afe2c6ff4
DOI: 10.1101/2021.03.05.433991

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Predicting functional consequences of mutations using molecular interaction network featuresDOI 10.1101/2021.03.05.433991Read

Predicting functional consequences of mutations using molecular interaction network features.Semantic proximity Read

Human Proteome-wide Mechanistic Interpretation of Missense Variants through Protein Feature Enrichment ScoreSemantic proximity Read

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect predictionSemantic proximity Read

Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutationsSemantic proximity Read

PON-P: integrated predictor for pathogenicity of missense variants.Semantic proximity Read

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature setSemantic proximity Read

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features.Semantic proximity Read

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure.Semantic proximity Read

AlphaMissense is better correlated with functional assays of missense impact than earlier prediction algorithmsSemantic proximity Read

Decoding molecular mechanisms for loss-of-function variants in the human proteomeSemantic proximity Read

DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic featuresSemantic proximity Read

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis.Semantic proximity Read