AIMC Topic: Genome, Plant
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Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.
PloS one
Aug 28, 2024
We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidops...
Deep learning can predict subgenome dominance in ancient but not in neo/synthetic polyploidized genomes.
The Plant journal : for cell and molecular biology
Aug 12, 2024
Deep learning offers new approaches to investigate the mechanisms underlying complex biological phenomena, such as subgenome dominance. Subgenome dominance refers to the dominant expression and/or biased fractionation of genes in one subgenome of all...
Analyzing Medicago spp. seed morphology using GWAS and machine learning.
Scientific reports
Jul 30, 2024
Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 oth...
Machine learning for genomic and pedigree prediction in sugarcane.
The plant genome
Jun 26, 2024
Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome predicti...
Using a hybrid neural network architecture for DNA sequence representation: A study on N-methylcytosine sites.
Computers in biology and medicine
May 27, 2024
N-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous inv...
Enhancing genome-wide populus trait prediction through deep convolutional neural networks.
The Plant journal : for cell and molecular biology
May 13, 2024
As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genot...
PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics.
Genes
May 9, 2024
As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizin...
Deep learning the cis-regulatory code for gene expression in selected model plants.
Nature communications
Apr 25, 2024
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predict...
BreedingAIDB: A database integrating crop genome-to-phenotype paired data with machine learning tools applicable to breeding.
Plant communications
Apr 3, 2024
Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.
PloS one
Jan 26, 2022
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions...
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