AIMC Topic: Recombination, Genetic
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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...
Predicting the Landscape of Recombination Using Deep Learning.
Molecular biology and evolution
Jun 1, 2020
Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recom...
An equivariant Bayesian convolutional network predicts recombination hotspots and accurately resolves binding motifs.
Bioinformatics (Oxford, England)
Jul 1, 2019
MOTIVATION: Convolutional neural networks (CNNs) have been tremendously successful in many contexts, particularly where training data are abundant and signal-to-noise ratios are large. However, when predicting noisily observed phenotypes from DNA seq...
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...
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