AIMC Topic: Genetics, Population

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Opening the Black Box: Interpretable Machine Learning for Geneticists.

Trends in genetics : TIG Apr 17, 2020
Because of its ability to find complex patterns in high dimensional and heterogeneous data, machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available. While the complexity of ML ...
Computational Biology Genetics, Medical Genome, Human Genetics, Population Humans Machine Learning
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Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.

Nature communications Mar 26, 2020
The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on compl...
Japan Base Sequence Multifactor Dimensionality Reduction Multifactorial Inheritance Principal Component Analysis Asian People Biological Specimen Banks Risk Factors Genetic Predisposition to Disease Genetics, Population Humans Phenotype
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ImaGene: a convolutional neural network to quantify natural selection from genomic data.

BMC bioinformatics Nov 22, 2019
BACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determini...
Alleles Algorithms Selection, Genetic Phenotype Genetics, Population Genomics Humans Databases, Genetic Neural Networks, Computer Software
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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 ...
Polymorphism, Single Nucleotide Proteome Models, Genetic Alleles Autism Spectrum Disorder Neoplasms Genomics Selection, Genetic Genetics, Population Inheritance Patterns Genetic Fitness Genetic Variation Base Sequence Genome, Human Humans Machine Learning
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A single nucleotide polymorphism panel for individual identification and ancestry assignment in Caucasians and four East and Southeast Asian populations using a machine learning classifier.

Forensic science, medicine, and pathology Jan 16, 2019
Single nucleotide polymorphism (SNP) profiling is an effective means of individual identification and ancestry inferences in forensic genetics. This study established a SNP panel for the simultaneous individual identification and ancestry assignment ...
Chromosomes, Human, Y Genetics, Population DNA Fingerprinting Chromosomes, Human, X DNA Degradation, Necrotic Asian People White People Gene Frequency Asia Genotype Support Vector Machine DNA, Mitochondrial Humans Machine Learning Male Polymorphism, Single Nucleotide Retrospective Studies Female
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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...
Computer Simulation Animals Models, Genetic Genetic Variation Haplotypes Evolution, Molecular Supervised Machine Learning Software Gene Flow Species Specificity Genetic Speciation Genetics, Population Drosophila Drosophila simulans Genome, Insect Hybridization, Genetic
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Supervised Machine Learning for Population Genetics: A New Paradigm.

Trends in genetics : TIG Jan 10, 2018
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly be...
Selection, Genetic Biological Evolution Genetics, Population Data Mining Datasets as Topic Genome, Human Humans Supervised 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 Adaptation, Biological Genetic Variation Humans Selection, Genetic Genome, Human Genetics, Population Machine Learning Linkage Disequilibrium Biological Evolution
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Deep Learning for Population Genetic Inference.

PLoS computational biology Mar 28, 2016
Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning,...
Models, Genetic Machine Learning Drosophila melanogaster Animals Genetics, Population Genomics
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S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning.

PLoS genetics Mar 15, 2016
Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over...
Genetic Drift Humans Genetics, Population Genome, Human Machine Learning Chromosomes, Human, Pair 18 Selection, Genetic Haplotypes
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