AI Medical Compendium Topic

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Mutation, Missense

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Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches.

Scientific reports
Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce p...

MVP predicts the pathogenicity of missense variants by deep learning.

Nature communications
Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. H...

Machine learning-based identification and characterization of 15 novel pathogenic SUOX missense mutations.

Molecular genetics and metabolism
Isolated sulfite oxidase deficiency (ISOD) is a rare hereditary metabolic disease caused by absence of functional sulfite oxidase (SO) due to mutations of the SUOX gene. SO oxidizes toxic sulfite and sulfite accumulation is associated with neurologic...

PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.

Communications biology
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug r...

Clinical feature-related single-base substitution sequence signatures identified with an unsupervised machine learning approach.

BMC medical genomics
BACKGROUND: Mutation processes leave different signatures in genes. For single-base substitutions, previous studies have suggested that mutation signatures are not only reflected in mutation bases but also in neighboring bases. However, because of th...

Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization.

Scientific reports
Among an assortment of genetic variations, Missense are major ones which a small subset of them may led to the upset of the protein function and ultimately end in human diseases. Various machine learning methods were declared to differentiate deleter...

Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning.

International journal of molecular sciences
Tat is an essential gene for increasing the transcription of all HIV genes, and affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo and produces variants with diverse activities, contributing ...

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning.

Journal of molecular and cellular cardiology
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...

Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

Bioinformatics (Oxford, England)
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...

Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.

Scientific reports
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 ...