AI Medical Compendium Topic

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Mutation, Missense

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Association Between Genotypes and Risk of Cardiovascular Disease in MESA (Multi-Ethnic Study of Atherosclerosis).

Journal of the American Heart Association
BACKGROUND: genetic variants confer an increased risk for kidney disease. Their associations with cardiovascular disease (CVD) are less certain. We aimed to compare the prevalence of subclinical CVD and incidence of atherosclerotic CVD and heart fai...

Applying an artificial neural network model for developing a severity score for patients with hereditary amyloid polyneuropathy.

Amyloid : the international journal of experimental and clinical investigation : the official journal of the International Society of Amyloidosis
Hereditary (familial) amyloid polyneuropathy (FAP) is a systemic disease that includes a sensorimotor polyneuropathy related to transthyretin (TTR) mutations. So far, a scale designed to classify the severity of this disease has not yet been validate...

An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins.

International journal of biological macromolecules
Newly synthesized polypeptides must pass stringent quality controls in cells to ensure appropriate folding and function. However, mutations, environmental stresses and aging can reduce efficiencies of these controls, leading to accumulation of protei...

Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors.

IEEE/ACM transactions on computational biology and bioinformatics
Transcription factors (TFs) are the major components of human gene regulation. In particular, they bind onto specific DNA sequences and regulate neighborhood genes in different tissues at different developmental stages. Non-synonymous single nucleoti...

Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function.

Journal of cellular biochemistry
Genetic polymorphisms are mostly associated with inherited diseases, detecting and analyzing the biological significance of functional single-nucleotide polymorphisms (SNPs) using wet laboratory experiments is an arduous task hence the computational ...

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Nucleic acids research
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...

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding aff...

LEAP: Using machine learning to support variant classification in a clinical setting.

Human mutation
Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogeni...

Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

American journal of human genetics
Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes ris...