AIMC Topic: Genetic Variation

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Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction.

Scientific reports Jan 24, 2019
Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudina...
Electronic Health Records Female Risk Factors Adult Humans Neural Networks, Computer Machine Learning Algorithms Deep Learning United States Longitudinal Studies Genetic Variation Cross-Sectional Studies Male Cardiovascular Diseases Case-Control Studies
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Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Trends in microbiology Dec 13, 2018
Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement ...
Ecosystem Genetic Variation Machine Learning Bacteria Metagenomics Environmental Monitoring DNA Barcoding, Taxonomic Microbiota Environmental Microbiology
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Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Risk analysis : an official publication of the Society for Risk Analysis Nov 21, 2018
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...
Humans Machine Learning Algorithms Food Microbiology Foodborne Diseases Listeria monocytogenes Virulence Phenotype Sensitivity and Specificity Risk Assessment Databases, Genetic Linear Models Food Listeriosis Genetic Variation High-Throughput Nucleotide Sequencing Probability
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Towards better prediction of Mycobacterium tuberculosis lineages from MIRU-VNTR data.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases Jun 28, 2018
The determination of lineages from strain-based molecular genotyping information is an important problem in tuberculosis. Mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) typing is a commonly used molecular genotyp...
Minisatellite Repeats Databases, Nucleic Acid New York City Interspersed Repetitive Sequences Humans Machine Learning Phylogeny Computational Biology Tuberculosis Genetic Variation Mycobacterium tuberculosis Molecular Epidemiology
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Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.

PLoS genetics Apr 23, 2018
Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is cru...
Drosophila simulans Gene Flow Genetic Variation Haplotypes Species Specificity Drosophila Hybridization, Genetic Genetics, Population Genome, Insect Genetic Speciation Animals Models, Genetic Software Evolution, Molecular Computer Simulation Supervised Machine Learning
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A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

BMC genomics Apr 17, 2018
BACKGROUND: Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. Wi...
Models, Statistical Genetic Variation High-Throughput Nucleotide Sequencing Base Sequence Machine Learning
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Selection plays the hand it was dealt: evidence that human adaptation commonly targets standing genetic variation.

Genome biology Aug 1, 2017
Using a powerful machine learning approach, a recent study of human genomes has revealed widespread footprints of recent positive selection on standing genetic variation.
Models, Genetic Sequence Analysis, DNA Genetic Variation Genome, Human Selection, Genetic Linkage Disequilibrium Biological Evolution Adaptation, Biological Humans Machine Learning Genetics, Population
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Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants.

Scientific reports Jun 7, 2017
Disease and trait-associated variants represent a tiny minority of all known genetic variation, and therefore there is necessarily an imbalance between the small set of available disease-associated and the much larger set of non-deleterious genomic v...
Software Genetic Variation Algorithms RNA, Untranslated Reproducibility of Results Genome-Wide Association Study Humans Genetic Predisposition to Disease Machine Learning Models, Genetic Mutation
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Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster.

Scientific reports May 25, 2017
The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identify...
Drosophila melanogaster Gene Ontology Quantitative Trait Loci Quantitative Trait, Heritable Models, Statistical Models, Biological Genetic Variation Inheritance Patterns Algorithms Coma Computer Simulation Animals Genome-Wide Association Study
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SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.

BMC genomics Nov 14, 2016
BACKGROUND: Next-generation sequencing (NGS) allows unbiased, in-depth interrogation of cancer genomes. Many somatic variant callers have been developed yet accurate ascertainment of somatic variants remains a considerable challenge as evidenced by t...
Workflow Genetic Variation High-Throughput Nucleotide Sequencing Web Browser Reproducibility of Results Machine Learning Software Polymorphism, Single Nucleotide Algorithms Computational Biology Mutation
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