AIMC Topic: Mutation, Missense

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

American journal of human genetics Sep 9, 2020
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...
Genetic Predisposition to Disease Cluster Analysis Hepatocyte Nuclear Factor 1-alpha Unsupervised Machine Learning Male Adolescent Child Diabetes Mellitus, Type 2 Datasets as Topic United Kingdom Norway Exome Sequencing Mutation, Missense Principal Component Analysis Gene Expression Registries Alleles Humans Phenotype Female Young Adult Adult
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LEAP: Using machine learning to support variant classification in a clinical setting.

Human mutation Apr 1, 2020
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...
Models, Statistical Area Under Curve Mutation, Missense ROC Curve Humans Genomics Logistic Models Machine Learning Models, Genetic Cardiovascular Diseases Neoplasms
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High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research Dec 23, 2019
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...
Software Algorithms Neoplasms Protein Binding Humans Neural Networks, Computer Artificial Intelligence Predictive Value of Tests Histocompatibility Antigens Class I Data Mining Mutation, Missense Histocompatibility Antigens Class II Computational Biology Cancer Vaccines Antigens, Neoplasm CD8-Positive T-Lymphocytes
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Computational and artificial neural network based study of functional SNPs of human LEPR protein associated with reproductive function.

Journal of cellular biochemistry Jun 25, 2019
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 ...
Infertility Humans Neural Networks, Computer Molecular Docking Simulation Polymorphism, Single Nucleotide Protein Domains Amino Acid Substitution Mutation, Missense Receptors, Leptin
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Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors.

IEEE/ACM transactions on computational biology and bioinformatics Nov 21, 2018
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 Biology Transcription Factors Data Mining Mutation, Missense Humans Machine Learning Algorithms Polymorphism, Single Nucleotide
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An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins.

International journal of biological macromolecules Jun 24, 2018
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...
Algorithms Support Vector Machine Amino Acid Substitution Proteins Mutation, Missense Protein Structure, Secondary
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Gene-Specific Variant Classifier (DPYD-Varifier) to Identify Deleterious Alleles of Dihydropyrimidine Dehydrogenase.

Clinical pharmacology and therapeutics Feb 2, 2018
Deleterious variants in dihydropyrimidine dehydrogenase (DPD, DPYD gene) can be highly predictive of clinical toxicity to the widely prescribed chemotherapeutic 5-fluorouracil (5-FU). However, there are very limited data pertaining to the functional ...
Risk Assessment Pharmacogenetics Genotype Dose-Response Relationship, Drug Cell Survival Predictive Value of Tests Structure-Activity Relationship Models, Molecular Humans Machine Learning Computer Simulation Dihydrouracil Dehydrogenase (NADP) Pharmacogenomic Testing Pharmacogenomic Variants Fluorouracil Gene Frequency Inhibitory Concentration 50 Protein Conformation HCT116 Cells Antimetabolites, Antineoplastic HEK293 Cells 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 Dec 21, 2017
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...
Apolipoprotein L1 Mutation, Missense Middle Aged Aged, 80 and over Humans Prevalence DNA Cross-Sectional Studies Female Incidence Atherosclerosis Risk Factors Male Genotype Ethnicity Aged Cardiovascular Diseases DNA Mutational Analysis
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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 Jul 18, 2017
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...
Amyloid Neuropathies, Familial Models, Biological Prealbumin Amino Acid Substitution Mutation, Missense Neural Networks, Computer Female Male Severity of Illness Index Humans
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Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides.

BMC bioinformatics Mar 7, 2017
BACKGROUND: Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset ...
Sensitivity and Specificity Humans Peptides Mitochondria Computational Biology Machine Learning DNA Mutational Analysis DNA, Mitochondrial Mutation, Missense
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