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Selection, Genetic

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Attribute selection and model evaluation for the maternal and paternal imprinted genes in bovine (Bos Taurus) using supervised machine learning algorithms.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
Imprinted genes display biased expression of paternal and maternal alleles in mammals. They are marked through epigenetic process during gametogenesis. Characterization of imprinted genes has expanded our understanding of the regulation and function ...

NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans.

Genome biology
State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with modest accuracy and strong positional biases. In this study, we curated 737 high-confidence pathogen...

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

G3 (Bethesda, Md.)
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes...

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.

Genome research
A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called inear llele-pecific election nferenc ...

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...

Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity.

PloS one
Determination of pile bearing capacity is essential in pile foundation design. This study focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network (DLNN) algorithm to predict the bearing capacity of driven pile. For this...

A review of deep learning applications for genomic selection.

BMC genomics
BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years,...

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images.

Journal of animal science
Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reac...

Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning.

Molecular biology and evolution
Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to d...

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...