AIMC Topic: Triticum

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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...
Triticum Gene-Environment Interaction Neural Networks, Computer Models, Genetic Phenotype Area Under Curve ROC Curve Zea mays Genomics Polymorphism, Single Nucleotide
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Early detection of germinated wheat grains using terahertz image and chemometrics.

Scientific reports Feb 19, 2016
In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtai...
Principal Component Analysis Germination Triticum Time Factors Terahertz Spectroscopy Neural Networks, Computer Support Vector Machine Least-Squares Analysis
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Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.

The plant genome Jun 1, 2025
Achieving significant genetic gains in grain yield (GY) in wheat (Triticum aestivum L.) requires optimization of the key biomass partitioning traits such as spike partitioning index (SPI) and fruiting efficiency (FE). However, traditional manual phen...
Phenotype Triticum Edible Grain Plant Breeding Genomics Models, Genetic Biomass
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Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development.

Food research international (Ottawa, Ont.) May 1, 2025
In recent years, the research and development (R&D) of rice and wheat functional foods has attracted a widespread attention from food researchers, driven by the increasing global food consumption and growing consumer demand for healthier and safer fo...
Food Technology Machine Learning Humans Triticum Food Handling Food Quality Artificial Intelligence Oryza Functional Food
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WheatGP, a genomic prediction method based on CNN and LSTM.

Briefings in bioinformatics Mar 4, 2025
Wheat plays a crucial role in ensuring food security. However, its complex genetic structure and trait variation pose significant challenges for breeding superior varieties. In this study, a genomic prediction method for wheat (WheatGP) is proposed. ...
Genome, Plant Plant Breeding Genomics Models, Genetic Triticum Neural Networks, Computer Phenotype
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Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.

The plant genome Mar 1, 2025
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide pol...
Triticum Hyperspectral Imaging Heat-Shock Response Remote Sensing Technology Genomics Machine Learning Polymorphism, Single Nucleotide
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Assessing the performance of generative artificial intelligence in retrieving information against manually curated genetic and genomic data.

Database : the journal of biological databases and curation Feb 17, 2025
Curated resources at centralized repositories provide high-value service to users by enhancing data veracity. Curation, however, comes with a cost, as it requires dedicated time and effort from personnel with deep domain knowledge. In this paper, we ...
Genomics Generative Artificial Intelligence Databases, Genetic Humans Artificial Intelligence Data Curation Genome, Plant Quantitative Trait Loci Data Mining Hordeum Triticum
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MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.

Journal of experimental botany Jan 10, 2025
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we i...
Deep Learning Magnetic Resonance Imaging Triticum Hordeum Seeds Algorithms Phenotype
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LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats.

Briefings in bioinformatics Nov 22, 2024
Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, ...
Deep Learning Computational Biology Software Genetic Variation Genome, Plant Epigenomics Triticum
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Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

G3 (Bethesda, Md.) Nov 6, 2024
In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, g...
Quantitative Trait Loci Models, Genetic Triticum Phenotype Animals Algorithms Genomics Polymorphism, Single Nucleotide Genome-Wide Association Study Neural Networks, Computer
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