Category: Computational Models
Improving crop modeling using machine learning
Integration of machine learning and crop modelling can optimize predictions of plant growth and yield.
Modelling pollen competition
Identification of traits affecting pollen performance
New open source software for identifying candidate genes
Introducing cageminer, an R/Bioconductor package to prioritize candidate genes by integrating GWAS and gene coexpression networks
Simulating red:far red ratio in your model
The best method depends on your goal, need for accuracy, and computing allowance
Autonomous construction of 3D leaves
An assessment of deep generative networks in creating realistic 3D data
Machine learning used to identify transcription factor-DNA interactions
Surveying machine learning methods to improve detection of binding sites.
Modelling perennial grass phenology
Predicting phenology can provide insight into better management.
Increased soybean yields finally achieved
Bioengineering boosts photosynthesis and increases yields in food crops for the first time ever.
Connecting rhythmic gene circuits to metabolism explains the slow growth rate of a slow-clock mutant
A model designed to study circadian effects on physiology becomes more broadly useful when the clock regulates metabolites.
The benefits of a network view of selection response for complex traits
A new gene-to-phenotype network for shoot branching.
Video-based measurements of maize stalk movement
Measuring the impact of brace roots on lodging resistance in maize.
Using models to optimize planting density
The closeness of plants affects architectural plasticity and carbon uptake.
Unraveling the mechanisms of producing high quality cotton
A new study uses computational modelling to determine how and why cotton quality develops.