AIMC Topic: Genetic Variation

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Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

Genome research Jun 27, 2019
A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called inear llele-pecific election nferenc ...
Neoplasms Genomics Polymorphism, Single Nucleotide Proteome Humans Machine Learning Autism Spectrum Disorder Genome, Human Selection, Genetic Genetics, Population Inheritance Patterns Genetic Fitness Models, Genetic Alleles Genetic Variation Base Sequence
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Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

EBioMedicine Apr 29, 2019
BACKGROUND: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited s...
Humans Extensively Drug-Resistant Tuberculosis ROC Curve Genomics Computational Biology Cluster Analysis Databases, Genetic Models, Statistical Machine Learning Evolution, Molecular Prognosis Antitubercular Agents Reproducibility of Results Tuberculosis, Multidrug-Resistant Microbial Sensitivity Tests Genetic Variation Mycobacterium tuberculosis Genome, Bacterial
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MMSplice: modular modeling improves the predictions of genetic variant effects on splicing.

Genome biology Mar 1, 2019
Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping prediction challenge. The...
Models, Genetic Genetic Variation Genetic Diseases, Inborn Alternative Splicing Neural Networks, Computer
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DeepPVP: phenotype-based prioritization of causative variants using deep learning.

BMC bioinformatics Feb 6, 2019
BACKGROUND: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity ...
Software Phenotype Deep Learning Genetic Variation Humans Neural Networks, Computer Gene Frequency Exome Sequencing Exome
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Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis.

Genetics in medicine : official journal of the American College of Medical Genetics Jan 24, 2019
PURPOSE: Despite the successful progress next-generation sequencing technologies has achieved in diagnosing the genetic cause of rare Mendelian diseases, the current diagnostic rate is still far from satisfactory because of heterogeneity, imprecision...
Rare Diseases Exome Phenotype Genomics Genotype Computational Biology Humans Mutation Machine Learning Genetic Testing Software Genetic Variation High-Throughput Nucleotide Sequencing
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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...
Genetic Variation Case-Control Studies Deep Learning United States Longitudinal Studies Machine Learning Humans Algorithms Neural Networks, Computer Electronic Health Records Cross-Sectional Studies Risk Factors Female Male Adult Cardiovascular Diseases
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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 ...
Machine Learning Ecosystem Bacteria Environmental Monitoring DNA Barcoding, Taxonomic Microbiota Environmental Microbiology Genetic Variation Metagenomics
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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...
High-Throughput Nucleotide Sequencing Probability Linear Models Food Listeriosis Foodborne Diseases Listeria monocytogenes Virulence Genetic Variation Sensitivity and Specificity Humans Risk Assessment Machine Learning Databases, Genetic Algorithms Food Microbiology Phenotype
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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...
Databases, Nucleic Acid Humans Tuberculosis New York City Machine Learning Genetic Variation Interspersed Repetitive Sequences Phylogeny Mycobacterium tuberculosis Minisatellite Repeats Computational Biology 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...
Genome, Insect Software Evolution, Molecular Species Specificity Supervised Machine Learning Computer Simulation Genetic Speciation Drosophila Genetic Variation Animals Drosophila simulans Hybridization, Genetic Haplotypes Models, Genetic Gene Flow Genetics, Population
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