AIMC Topic: Quantitative Trait, Heritable

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Improving genetic variant identification for quantitative traits using ensemble learning-based approaches.

BMC genomics Mar 12, 2025
BACKGROUND: Genome-wide association studies (GWAS) are rapidly advancing due to the improved resolution and completeness provided by Telomere-to-Telomere (T2T) and pangenome assemblies. While recent advancements in GWAS methods have primarily focused...
Humans Machine Learning Phenotype Quantitative Trait Loci Ensemble Learning Quantitative Trait, Heritable Genome-Wide Association Study Polymorphism, Single Nucleotide Genetic Variation
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Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.

PloS one Aug 28, 2024
We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidops...
Quantitative Trait Loci Quantitative Trait, Heritable Machine Learning Arabidopsis Phenotype Genomics Models, Genetic Neural Networks, Computer Linear Models Genome, Plant
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SOMmelier-Intuitive Visualization of the Topology of Grapevine Genome Landscapes Using Artificial Neural Networks.

Genes Jul 17, 2020
BACKGROUND: Whole-genome studies of vine cultivars have brought novel knowledge about the diversity, geographical relatedness, historical origin and dissemination, phenotype associations and genetic markers.
Databases, Genetic Vitis Polymorphism, Genetic Neural Networks, Computer Genome-Wide Association Study Genome, Plant Quantitative Trait, Heritable
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Predicting personality from patterns of behavior collected with smartphones.

Proceedings of the National Academy of Sciences of the United States of America Jul 14, 2020
Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media co...
Machine Learning Reproducibility of Results Personality Humans Smartphone Social Behavior Models, Theoretical Quantitative Trait, Heritable Privacy
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KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters.

Genome biology Jun 17, 2020
Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used in animal and plant breeding, and increasingly in human genetics. Inspired by the efficient computing...
Quantitative Trait, Heritable Software Animals Cattle Genomics Models, Genetic Models, Statistical Humans Machine Learning
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Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.

Genetics, selection, evolution : GSE Feb 24, 2020
BACKGROUND: Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine...
Genomics Polymorphism, Single Nucleotide Genotype Models, Genetic Bayes Theorem Quantitative Trait, Heritable Animals Deep Learning Cattle Phenotype Multifactorial Inheritance
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Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie Feb 5, 2020
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore c...
Quantitative Trait, Heritable Genome Linkage Disequilibrium Chromosomes Genotype Cattle Bayes Theorem Neural Networks, Computer Genomics Meat Gene Frequency Animals Polymorphism, Single Nucleotide Breeding Pedigree
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Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

G3 (Bethesda, Md.) Dec 3, 2019
Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and ...
Gene Expression Regulation, Plant Genome-Wide Association Study Machine Learning Quantitative Trait, Heritable Plant Leaves Zea mays
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QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice.

G3 (Bethesda, Md.) Oct 7, 2019
Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine ma...
Quantitative Trait Loci Genes, Plant Quantitative Trait, Heritable ROC Curve Computational Biology Oryza Machine Learning Web Browser Software Arabidopsis Algorithms Reproducibility of Results
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fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.

BMC bioinformatics Jun 13, 2019
BACKGROUND: Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alt...
Computer Simulation Machine Learning Blood Proteins Genome-Wide Association Study Algorithms Quantitative Trait, Heritable Polymorphism, Single Nucleotide
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