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TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik

Showing 1 to 9 of 9 articles

Improving plant breeding through AI-supported data integration.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Jun 2, 2025
Integrating, learning from, and predicting using vast datasets from various scales, platforms, and species is crucial for advancing crop improvement through breeding. Artificial intelligence (AI) is a broad category of methods, many of which have bee...
Phenotype Crops, Agricultural Plant Breeding Artificial Intelligence
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EBMGP: a deep learning model for genomic prediction based on Elastic Net feature selection and bidirectional encoder representations from transformer's embedding and multi-head attention pooling.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Apr 19, 2025
Enhancing early selection through genomic estimated breeding values is pivotal for reducing generation intervals and accelerating breeding programs. Recently, deep learning (DL) approaches have gained prominence in genomic prediction (GP). Here, we i...
Polymorphism, Single Nucleotide Plant Breeding Deep Learning Models, Genetic Genomics
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Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Jan 24, 2025
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic archit...
Flowers Gossypium Genome-Wide Association Study Cotton Fiber Quantitative Trait Loci Polymorphism, Single Nucleotide Machine Learning Phenotype
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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...
Genotype Models, Genetic Phenotype Genomics Environment Gene-Environment Interaction Plant Breeding Zea mays Edible Grain 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...
Models, Genetic Selection, Genetic Gene-Environment Interaction Deep Learning Genomics Algorithms Phenotype Neural Networks, Computer
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Predicting phenotypes from genetic, environment, management, and historical data using CNNs.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 27, 2021
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and man...
Phenotype Genomics Zea mays Data Mining Neural Networks, Computer Machine Learning Crops, Agricultural
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Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 3, 2021
Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughpu...
Genotype Spectroscopy, Near-Infrared Genetic Association Studies Water Zea mays Cooking Machine Learning
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Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik May 24, 2020
We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, buil...
Linkage Disequilibrium Chromosome Mapping Machine Learning Hordeum Algorithms Genome, Plant Genotype
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A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 4, 2018
Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new heuristic method for phasing and imputation of genomic data in diploid plant species. Ou...
Plant Breeding Haplotypes Computer Heuristics Genotype Genomics Plants Genetic Markers Software Polymorphism, Single Nucleotide Alleles Computer Simulation
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