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Genetics, Population

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ImaGene: a convolutional neural network to quantify natural selection from genomic data.

BMC bioinformatics
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...

Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.

Nature communications
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...

Opening the Black Box: Interpretable Machine Learning for Geneticists.

Trends in genetics : TIG
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 ...

Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation.

Molecular ecology resources
For the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong ...

Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm.

Sensors (Basel, Switzerland)
In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such as premature maturity, low convergence path quality, poor population diversity, and difficulty in breaking the local optimal solution, this paper prop...

How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data.

International journal of molecular sciences
The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F population were genotype...

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
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...

On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn't.

Genes
In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transfor...

Autosomal deletion/insertion polymorphisms for global stratification analyses and ancestry origin inferences of different continental populations by machine learning methods.

Electrophoresis
A lot of population data of 30 deletion/insertion polymorphisms (DIPs) of the Investigator DIPplex kit in different continental populations have been reported. Here, we assessed genetic distributions of these 30 DIPs in different continental populati...

RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.

Briefings in bioinformatics
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 ...