AIMC Topic: Mutation, Missense

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AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.

Protein science : a publication of the Protein Society Feb 1, 2025
Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences...
Humans Machine Learning Software Computational Biology Proteins Databases, Protein Models, Molecular Protein Conformation Mutation, Missense
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Discovering predisposing genes for hereditary breast cancer using deep learning.

Briefings in bioinformatics May 23, 2024
Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be iden...
Humans Female Breast Neoplasms Deep Learning Genetic Predisposition to Disease Pedigree Mutation, Missense Germ-Line Mutation
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Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease.

Human molecular genetics Jan 20, 2024
BACKGROUND: Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (c...
Humans Machine Learning Kidney Neoplasms Carcinoma, Renal Cell Mutation, Missense von Hippel-Lindau Disease Von Hippel-Lindau Tumor Suppressor Protein
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Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

Bioinformatics (Oxford, England) May 4, 2023
MOTIVATION: Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major...
Humans Artificial Intelligence Computational Biology Mutation Virulence Mutation, Missense
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mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Nucleic acids research Jul 2, 2019
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predic...
Humans Machine Learning Software Protein Binding Proteins Internet Benchmarking Datasets as Topic Crystallography, X-Ray Binding Sites Thermodynamics Mutation, Missense Protein Interaction Mapping Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Protein Conformation, beta-Strand
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