AIMC Topic: Mutation, Missense

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Accurate identification and mechanistic evaluation of pathogenic missense variants with .

Proceedings of the National Academy of Sciences of the United States of America May 2, 2025
Understanding the effects of missense mutations or single amino acid variants (SAVs) on protein function is crucial for elucidating the molecular basis of diseases/disorders and designing rational therapies. We introduce here , a machine learning too...
Computational Biology Humans Machine Learning Proteins Algorithms Databases, Protein Mutation, Missense
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A random forest-based predictive model for classifying BRCA1 missense variants: a novel approach for evaluating the missense mutations effect.

Journal of human genetics Apr 18, 2025
The right classification of variants is the key to pre-symptomatic detection of disease and conducting preventive actions. Since BRCA1 has a high incidence and penetrance in breast and ovarian cancers, a high-performance predictive tool can be employ...
Breast Neoplasms ROC Curve Random Forest Genetic Predisposition to Disease Computational Biology Mutation, Missense Humans BRCA1 Protein Ovarian Neoplasms Machine Learning Female
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Machine Learning and Structural Dynamics-Based Approach to Reveal Molecular Mechanism of PTEN Missense Mutations Shared by Cancer and Autism Spectrum Disorder.

Journal of chemical information and modeling Apr 14, 2025
Missense mutations in oncogenic proteins that are concurrently associated with neurodevelopmental disorders have garnered significant attention. Phosphatase and tensin homologue (PTEN) serves as a paradigmatic model for mapping its mutational landsca...
Humans Neoplasms Molecular Dynamics Simulation Protein Conformation Machine Learning Autism Spectrum Disorder Mutation, Missense PTEN Phosphohydrolase
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A novel seven-tier framework for the classification of MEFV missense variants using adaptive and rigid classifiers.

Scientific reports Mar 17, 2025
There is a great discrepancy between the clinical categorization of MEFV gene variants and in silico tool predictions. In this study, we developed a seven-tier classification system for MEFV missense variants of unknown significance and recommended a...
Familial Mediterranean Fever Pyrin Databases, Genetic Mutation, Missense Computer Simulation Machine Learning Computational Biology Algorithms Humans
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Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning.

Biomacromolecules Mar 12, 2025
We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification of large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). We used ∼6500 IDP s...
Intrinsically Disordered Proteins Mutation, Missense Deep Learning Molecular Dynamics Simulation Neural Networks, Computer Algorithms Humans
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Integrating Artificial Intelligence and Bioinformatics Methods to Identify Disruptive STAT1 Variants Impacting Protein Stability and Function.

Genes Mar 1, 2025
The Signal Transducer and Activator of Transcription 1 () gene is an essential component of the JAK-STAT signaling pathway. This pathway plays a pivotal role in the regulation of different cellular processes, including immune responses, cell growth,...
Mutation, Missense STAT1 Transcription Factor Polymorphism, Single Nucleotide Computational Biology Humans Protein Stability Artificial Intelligence
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Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

IEEE journal of biomedical and health informatics Feb 10, 2025
Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These ...
Neoplasms Antineoplastic Agents Computational Biology Mutation, Missense Deep Learning Humans Neural Networks, Computer
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Deep learning-based assessment of missense variants in the gene presented with bilateral congenital cataract.

BMJ open ophthalmology Jan 14, 2025
OBJECTIVE: We compared the protein structure and pathogenicity of clinically relevant variants of the gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time.
Mutation, Missense Deep Learning Cataract Phenotype Female Male Humans
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Toward trustable use of machine learning models of variant effects in the clinic.

American journal of human genetics Nov 18, 2024
There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part by progress in applying deep learning methods to sequence data. These models have the potential to e...
Deep Learning Models, Genetic Genetic Variation Humans Machine Learning Mutation, Missense Computer Simulation
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Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.

International journal of molecular sciences Oct 30, 2024
Understanding the effect of single-missense mutations on protein stability is crucial for clinical decision-making and therapeutic development. The impact of these mutations on protein stability and 3D structure remains underexplored. Here, we develo...
Eye Proteins cis-trans-Isomerases Eye Diseases Clinical Relevance Protein Stability Humans Mutation, Missense Machine Learning
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