AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
In this study, a dataset derived from a natural rice population was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework, which provided valuable insights into modeling genotype-by-envi...
This critical narrative review evaluates how genomics, pangenomics, transcriptomics, transcriptomics, epigenomics, proteomics, metabolomics, phenomics and environmental covariates are being integrated with statistical learning, machine learning and deep learning for crop improvement.
Abstract Genetic gains of oat (Avena sativa L.) grain yield have been historically low compared to other major cereal crops. The use of machine learning models to capture complex interactions and leveraging data types other than genomic information in prediction models has great potential for improving complex traits i...
Abstract Quinoa (Chenopodium quinoa Willd.) is gaining global importance for its nutritional value and adaptability; however, breeding progress remains limited. Genomic selection (GS), combined with rapid generation cycles, offers a strategy to accelerate genetic improvement. We conducted whole‐genome resequencing of 6...
Predictive breeding has been proposed as an effective approach to accelerate genetic gain for complex traits. Genomic prediction (GP) models have been developed in alfalfa ( Medicago sativa L.) for key traits in the last decade. More recently, phenomic prediction (PP) models have been proposed as a low‐cost, high...
Dermo disease caused by the protist Perkinsus marinus poses a major threat to Eastern oyster aquaculture. We previously conducted genomic selection for dermo resistance and found increased effectiveness over phenotypic selection. Here, we report improved genomic predictions using combined data from three successive gen...
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