AIMC Topic: Gene-Environment Interaction

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Performance of deep-learning-based approaches to improve polygenic scores.

Nature communications Jun 2, 2025
Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonl...
Polymorphism, Single Nucleotide Deep Learning Models, Genetic Genome-Wide Association Study United Kingdom Gene-Environment Interaction Humans Genetic Predisposition to Disease Neural Networks, Computer Multifactorial Inheritance Phenotype Linear Models Linkage Disequilibrium
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Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations.

Current osteoporosis reports May 24, 2025
PURPOSE OF REVIEW: Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underreprese...
Osteoporosis Genome-Wide Association Study Genomics Genetic Predisposition to Disease Humans Machine Learning Bone Density Gene-Environment Interaction Osteoporotic Fractures African People Low Density Lipoprotein Receptor-Related Protein-5 Core Binding Factor Alpha 1 Subunit Asian People White People
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Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Mar 6, 2025
Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framewo...
Machine Learning Phenotype Zea mays Environment Genome, Plant Gene-Environment Interaction Genome-Wide Association Study Genomics Genotype
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Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth.

Communications biology Dec 24, 2024
A complex interplay of genetic and environmental factors influences bacterial growth. Understanding these interactions is crucial for insights into complex living systems. This study employs a data-driven approach to uncover the principles governing ...
Machine Learning Gene Expression Regulation, Bacterial Environment Gene Regulatory Networks Gene-Environment Interaction Escherichia coli Culture Media
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Machine learning algorithms translate big data into predictive breeding accuracy.

Trends in plant science Oct 26, 2024
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability...
Plant Breeding Big Data Genomics Gene-Environment Interaction Algorithms Phenotype Machine Learning
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Gene-diet interactions in carbonated sugar-sweetened beverage consumption and metabolic syndrome risk: A machine learning analysis in a large hospital-based cohort.

Clinical nutrition ESPEN Oct 11, 2024
BACKGROUND AND AIM: Carbonated sugar-sweetened beverages (CSSB) intake has been increasingly linked to metabolic diseases. To investigate the association between CSSB intake and metabolic syndrome (MetS) risk, and the interaction between genetic pred...
Sugar-Sweetened Beverages Feeding Behavior Carbonated Beverages Gene-Environment Interaction Genetic Predisposition to Disease Polymorphism, Single Nucleotide Metabolic Syndrome Diet Genome-Wide Association Study Cohort Studies Humans Machine Learning Female Male Risk Factors Middle Aged Adult
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Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Jul 23, 2024
Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with hi...
Models, Genetic Zea mays Edible Grain Plant Breeding Environment Gene-Environment Interaction Phenotype Genomics Genotype Machine Learning
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Residual networks without pooling layers improve the accuracy of genomic predictions.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik May 21, 2024
Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction ac...
Selection, Genetic Phenotype Gene-Environment Interaction Deep Learning Genomics Neural Networks, Computer Models, Genetic Algorithms
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Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Journal of the Formosan Medical Association = Taiwan yi zhi Dec 2, 2023
BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.
Support Vector Machine Neural Networks, Computer Humans Alzheimer Disease Machine Learning Case-Control Studies Mitochondrial Precursor Protein Import Complex Proteins Sensitivity and Specificity Gene-Environment Interaction ROC Curve Female Male Aged Polymorphism, Single Nucleotide Aged, 80 and over
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Dynamic parameter identification and adaptive control with trajectory scaling for robot-environment interaction.

PloS one Jul 13, 2023
To improve the force/position control performance of robots in contact with the environment, this paper proposes a control scheme comprising dynamic parameter identification, trajectory scaling, and computed-torque control based on adaptive parameter...
Least-Squares Analysis Algorithms Gene-Environment Interaction Computer Simulation Robotics
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