Using a powerful machine learning approach, a recent study of human genomes has revealed widespread footprints of recent positive selection on standing genetic variation.
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,...
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...
Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based app...
MOTIVATION: Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populati...
Genetics and molecular research : GMR
Jun 18, 2015
The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, ...
Standard methods for estimating the population recombination parameter, ρ, are dependent on sampling individual genotypes and calculating various types of disequilibria. However, recent machine learning (ML) approaches to estimating recombination hav...
Tools like Snipper or the Admixture Model count as state-of-the-art methods in forensic science for biogeographical ancestry. However, they have not been systematically compared to classifiers widely used in other disciplines. Noting that genetic dat...
Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter t...
As population genetic data increase in size, new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient but are not interchangeable with exis...
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