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Recombination, Genetic

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The Prediction of Recombination Hotspot Based on Automated Machine Learning.

Journal of molecular biology
Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot predict...

Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core-Substituent Recombination.

ChemMedChem
The use of black box machine learning models whose decisions cannot be understood limits the acceptance of predictions in interdisciplinary research and camouflages artificial learning characteristics leading to predictions for other than anticipated...

Charge Recombination Dynamics in a Metal Halide Perovskite Simulated by Nonadiabatic Molecular Dynamics Combined with Machine Learning.

The journal of physical chemistry letters
Nonadiabatic coupling (NAC) plays a central role in driving nonadiabatic dynamics in various photophysical and photochemical processes. However, the high computational cost of NAC limits the time scale and system size of quantum dynamics simulation. ...

Deep learning identifies and quantifies recombination hotspot determinants.

Bioinformatics (Oxford, England)
MOTIVATION: Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known...

A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Molecular biology and evolution
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
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)
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...

iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components.

Genomics
Recombination spot identification plays an important role in revealing genome evolution and developing DNA function study. Although some computational methods have been proposed, extracting discriminatory information embedded in DNA properties has no...

The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference.

Molecular biology and evolution
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...