AIMC Topic: Plant Breeding
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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...
Genomic selection: Essence, applications, and prospects.
The plant genome
Jun 1, 2025
Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype-phenotype relationship...
Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
May 29, 2025
Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food...
Precise genome editing process and its applications in plants driven by AI.
Functional & integrative genomics
May 24, 2025
Genome editing technologies have emerged as the keystone of biotechnological research, enabling precise gene modification. The field has evolved rapidly through revolutionary advancements, transitioning from early explorations to the breakthrough of ...
Post-composing ontology terms for efficient phenotyping in plant breeding.
Database : the journal of biological databases and curation
Mar 21, 2025
Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on...
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. ...
Integration of machine learning and genome-wide association study to explore the genomic prediction accuracy of agronomic trait in oats (Avena sativa L.).
The plant genome
Mar 1, 2025
Machine learning (ML) has garnered significant attention for its potential to enhance the accuracy of genomic predictions (GPs) in various economic crops with the use of complete genomic information. Genome-wide association studies (GWAS) are widely ...
Tasselyzer, a machine learning method to quantify maize anther exertion, based on PlantCV.
The Plant journal : for cell and molecular biology
Feb 1, 2025
Maize anthers emerge from male-only florets, a process that involves complex genetic programming and is affected by environmental factors. Quantifying anther exertion provides a key indicator of male fertility; however, traditional manual scoring met...
SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.
Briefings in bioinformatics
Sep 22, 2023
Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. A...
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