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Plant Breeding

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Cropformer: An interpretable deep learning framework for crop genomic prediction.

Plant communications
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models ...

Synthetic biology and artificial intelligence in crop improvement.

Plant communications
Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved b...

Application of machine learning and genomics for orphan crop improvement.

Nature communications
Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources avail...

Tasselyzer, a machine learning method to quantify maize anther exertion, based on PlantCV.

The Plant journal : for cell and molecular biology
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...

Biological Prior Knowledge-Embedded Deep Neural Network for Plant Genomic Prediction.

Genes
Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed mode...

WheatGP, a genomic prediction method based on CNN and LSTM.

Briefings in bioinformatics
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. ...

Post-composing ontology terms for efficient phenotyping in plant breeding.

Database : the journal of biological databases and curation
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...

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
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 ...

Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI.

Scientific reports
Intelligent knowledge about Capsicum annuum L. germplasm could lead to effective management of germplasm. Here, 29 accessions of sweet pepper were investigated in two separate randomized complete block design with three replications in the field cond...

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
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...