AIMC Topic: Quantitative Trait, Heritable

Clear Filters Showing 21 to 27 of 27 articles

Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster.

Scientific reports May 25, 2017
The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identify...
Models, Statistical Models, Biological Genetic Variation Animals Genome-Wide Association Study Quantitative Trait, Heritable Inheritance Patterns Coma Drosophila melanogaster Gene Ontology Quantitative Trait Loci Algorithms Computer Simulation
View on PubMed DOI

Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

Genetics, selection, evolution : GSE Nov 7, 2016
BACKGROUND: Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a s...
Breeding Dairying Genome Quantitative Trait, Heritable Animal Feed Supervised Machine Learning Animal Nutritional Physiological Phenomena Models, Genetic Models, Biological Animals Phenotype Cattle Genomics Female
View on PubMed DOI

Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.

Genetics, selection, evolution : GSE Mar 31, 2015
BACKGROUND: Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can captur...
Male Cattle Polymorphism, Single Nucleotide Quantitative Trait, Heritable Genotype Lactation Milk Crosses, Genetic Linear Models Phenotype Neural Networks, Computer Dairying Breeding Algorithms Female Genome Animals
View on PubMed DOI

Brief Survey on Machine Learning in Epistasis.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interacti...
Humans Machine Learning Crohn Disease Phenotype Neoplasms Plants Epistasis, Genetic Macular Degeneration Quantitative Trait, Heritable Inheritance Patterns Data Mining Genome-Wide Association Study Alzheimer Disease Polymorphism, Single Nucleotide Genome, Human Computational Biology
View on PubMed DOI

DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.

GigaScience Mar 1, 2020
BACKGROUND: High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addres...
Quantitative Trait, Heritable Arabidopsis Deep Learning Software Models, Genetic Phenotype Fruit
View on PubMed DOI

Discovery of phenotypic networks from genotypic association studies with application to obesity.

International journal of data mining and bioinformatics Jan 1, 2015
Genome-wide Association Studies (GWAS) have resulted in many discovered risk variants for several obesity-related traits. However, before clinical relevance of these discoveries can be achieved, molecular or physiological mechanisms of these risk var...
Quantitative Trait, Heritable Male Algorithms Humans Genome-Wide Association Study Female Polymorphism, Single Nucleotide Artificial Intelligence Child Cross-Sectional Studies Obesity Databases, Nucleic Acid
View on PubMed DOI

Epistasis analysis using multifactor dimensionality reduction.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
Here we introduce the multifactor dimensionality reduction (MDR) methodology and software package for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the method and then highlight some of the ke...
Case-Control Studies Genome-Wide Association Study Genotype Epistasis, Genetic Urinary Bladder Neoplasms Humans Multifactor Dimensionality Reduction Statistics as Topic Quantitative Trait, Heritable Stochastic Processes
View on PubMed DOI
Popular Topics
Recent Journals