AIMC Topic: Gene-Environment Interaction

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Genome-enabled prediction using probabilistic neural network classifiers.

BMC genomics Mar 9, 2016
BACKGROUND: Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled prediction. Here, we evaluated and compared the classification performance of an MLP classifier versus that o...
Gene-Environment Interaction Polymorphism, Single Nucleotide Models, Genetic Zea mays Triticum Area Under Curve ROC Curve Genomics Neural Networks, Computer Phenotype
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Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

Gene Jan 16, 2016
In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how m...
Aged Adult Middle Aged Diet Breast Neoplasms Neural Networks, Computer Female Humans Disease Susceptibility Food Gene-Environment Interaction Xenobiotics Epistasis, Genetic Metabolic Networks and Pathways Case-Control Studies Folic Acid Computational Biology
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Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).

The plant genome Jun 1, 2025
For important food crops such as the common bean (Phaseolus vulgaris, L.), global demand continues to outpace the rate of genetic gain for quantitative traits. In this study, we leveraged the multi-environment trial (MET) dataset from the cooperative...
Polymorphism, Single Nucleotide Genotype Models, Genetic Phenotype Quantitative Trait Loci Gene-Environment Interaction Phaseolus Genomics Genome, Plant Plant Breeding
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A machine-learning approach to map landscape connectivity in with genetic and environmental data.

Proceedings of the National Academy of Sciences of the United States of America Mar 2, 2021
Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interact...
Humans Machine Learning Aedes Models, Biological Animals Gene-Environment Interaction Mosquito Vectors Workflow Genetics, Population Genetic Variation Environment
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Research progress in machine learning methods for gene-gene interaction detection.

Yi chuan = Hereditas Mar 20, 2018
Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to...
Machine Learning Animals Humans Gene-Environment Interaction Genome-Wide Association Study Gene Regulatory Networks
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[Detecting gene-gene/environment interactions by model-based multifactor dimensionality reduction].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi Nov 1, 2015
This paper introduces a method called model-based multifactor dimensionality reduction (MB-MDR), which was firstly proposed by Calle et al., and can be applied for detecting gene-gene or gene-environment interactions in genetic studies. The basic pri...
Gene-Environment Interaction Genotype Phenotype Models, Genetic Multifactor Dimensionality Reduction
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Application of artificial neural networks to link genetic and environmental factors to DNA methylation in colorectal cancer.

Epigenomics Jan 1, 2015
AIMS: We applied artificial neural networks (ANNs) to understand the connections among polymorphisms of genes involved in folate metabolism, clinico-pathological features and promoter methylation levels of MLH1, APC, CDKN2A(INK4A), MGMT and RASSF1A i...
Promoter Regions, Genetic Life Style Folic Acid Polymorphism, Genetic Colorectal Neoplasms Gene-Environment Interaction Female Male Humans Diet Middle Aged Aged Neural Networks, Computer DNA Methylation
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