AIMC Topic: Selection, Genetic

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Cotton genotypes selection through artificial neural networks.

Genetics and molecular research : GMR
Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explore...

How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation.

PLoS computational biology
One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can nat...

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability.

Genetics and molecular research : GMR
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean g...

Identifying targets of selection in mosaic genomes with machine learning: applications in Anopheles gambiae for detecting sites within locally adapted chromosomal inversions.

Molecular ecology
Chromosomal inversions are important structural changes that may facilitate divergent selection when they capture co-adaptive loci in the face of gene flow. However, identifying selection targets within inversions can be challenging. The high degrees...

Evaluation of the efficiency of artificial neural networks for genetic value prediction.

Genetics and molecular research : GMR
Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of t...

S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning.

PLoS genetics
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...

Hierarchical boosting: a machine-learning framework to detect and classify hard selective sweeps in human populations.

Bioinformatics (Oxford, England)
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...

Artificial neural networks reveal efficiency in genetic value prediction.

Genetics and molecular research : GMR
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, ...

DANN: a deep learning approach for annotating the pathogenicity of genetic variants.

Bioinformatics (Oxford, England)
UNLABELLED: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-c...