AIMC Topic: Mutation, Missense

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Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.

Nature communications Sep 27, 2024
Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To ad...
Machine Learning Computational Biology Mutation, Missense Humans Phase Separation Intrinsically Disordered Proteins
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AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Human genomics Sep 11, 2024
Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. L...
Software Female Breast Neoplasms Humans Artificial Intelligence Computational Biology Databases, Genetic Mutation, Missense Genetic Predisposition to Disease Polymorphism, Single Nucleotide
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Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques.

Biomolecules Sep 2, 2024
The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditiona...
Humans Deep Learning ROC Curve Computational Biology Proteins Mutation, Missense
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Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.

Human genomics Aug 27, 2024
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence align...
Software Computational Biology Humans Machine Learning Proteins Mutation, Missense
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PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.

Structure (London, England : 1993) Mar 19, 2024
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we...
Tumor Suppressor Protein p53 Mutation, Missense Myoglobin DNA Glycosylases Point Mutation Models, Molecular Protein Stability Neural Networks, Computer Databases, Protein Humans
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Prediction of protein structure and AI.

Journal of human genetics Jan 4, 2024
AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-functi...
Humans Models, Molecular Protein Conformation Protein Folding Mutation, Missense Computational Biology Artificial Intelligence Proteins Deep Learning
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Predicting pathogenic protein variants.

Science (New York, N.Y.) Sep 19, 2023
Machine-learning algorithm uses structure prediction to spot disease-causing mutations.
Protein Conformation Disease Mutant Proteins Mutation, Missense Sequence Analysis, DNA Genetic Variation Machine Learning Algorithms
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Cross-protein transfer learning substantially improves disease variant prediction.

Genome biology Aug 7, 2023
BACKGROUND: Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation m...
Humans Machine Learning Mutation Amino Acid Sequence Mutation, Missense Computational Biology Proteome
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Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.

Scientific reports Jun 28, 2023
Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of ...
Gene Frequency Mutation, Missense Machine Learning Neural Networks, Computer Virulence
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Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning.

Journal of molecular and cellular cardiology May 13, 2023
Congenital long QT syndrome (LQTS) is characterized by a prolonged QT-interval on an electrocardiogram (ECG). An abnormal prolongation in the QT-interval increases the risk for fatal arrhythmias. Genetic variants in several different cardiac ion chan...
Mutation, Missense KCNQ1 Potassium Channel Humans Phenotype Machine Learning Long QT Syndrome
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