AIMC Topic: Genetics, Population

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Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese.

Yi chuan = Hereditas Nov 20, 2022
Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic clas...
Republic of Korea Phylogeny Humans Machine Learning Japan Genetics, Population China Asian People
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KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Briefings in bioinformatics Jul 18, 2022
Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of princ...
Discriminant Analysis Supervised Machine Learning Genetics, Population Genetic Structures Polymorphism, Single Nucleotide Principal Component Analysis Humans
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A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Molecular biology and evolution Jan 7, 2022
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method t...
Deep Learning Genetics, Population Recombination, Genetic Models, Genetic Selection, Genetic
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RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.

Briefings in bioinformatics Nov 5, 2021
Genotype imputation is a statistical method for estimating missing genotypes from a denser haplotype reference panel. Existing methods usually performed well on common variants, but they may not be ideal for low-frequency and rare variants. Previous ...
Genetics, Population Genotyping Techniques Web Browser Genome-Wide Association Study Genotype Computational Biology Databases, Genetic Reproducibility of Results Deep Learning Algorithms Neural Networks, Computer Humans Software
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A machine-learning approach to map landscape connectivity in with genetic and environmental data.

Proceedings of the National Academy of Sciences of the United States of America Mar 2, 2021
Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interact...
Animals Genetics, Population Workflow Humans Environment Models, Biological Aedes Gene-Environment Interaction Genetic Variation Mosquito Vectors Machine Learning
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The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.

Molecular biology and evolution Feb 1, 2019
Population-scale genomic data sets have given researchers incredible amounts of information from which to infer evolutionary histories. Concomitant with this flood of data, theoretical and methodological advances have sought to extract information fr...
Hybridization, Genetic Animals Neural Networks, Computer Recombination, Genetic Selection, Genetic Genetics, Population
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Localizing and Classifying Adaptive Targets with Trend Filtered Regression.

Molecular biology and evolution Feb 1, 2019
Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information combined from several summary statistics typically assume no correlation of summary statistics...
Genetics, Population Software Genome, Human Computer Simulation Humans Genetic Techniques Models, Genetic Machine Learning Regression Analysis
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