AIMC Topic: Genes, Plant

Clear Filters Showing 1 to 10 of 22 articles

Evaluation of Lens culinaris germplasm against ascochyta blight by applying phenotyping and defense genes attributes.

Scientific reports
Lentil Ascochyta blight (caused by Ascochyta lentis) is one of the most important limiting factors of lentil cultivation and production in most regions of the world. Introducing resistance sources against the pathogen is a suitable strategy to conque...

Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.

Planta
AI-driven key gene prediction is revolutionizing crop breeding, enhancing precision, efficiency, and sustainability while paving the way for intelligent, data-driven agricultural innovation. The integration of artificial intelligence (AI) into crop b...

Biomarker genes for model-based prediction of drought-stress perception levels in rice.

BMC plant biology
BACKGROUND: Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resist...

PotatoG-DKB: a potato gene-disease knowledge base mined from biological literature.

PeerJ
BACKGROUND: Potato is the fourth largest food crop in the world, but potato cultivation faces serious threats from various diseases and pests. Despite significant advancements in research on potato disease resistance, these findings are scattered acr...

Utilizing machine learning and bioinformatics analysis to identify drought-responsive genes affecting yield in foxtail millet.

International journal of biological macromolecules
Drought stress is a major constraint on crop development, potentially causing huge yield losses and threatening global food security. Improving Crop's stress tolerance is usually associated with a yield penalty. One way to balance yield and stress to...

Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.

BMC genomics
BACKGROUND: Plant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on...

Deep learning-enabled discovery and characterization of HKT genes in Spartina alterniflora.

The Plant journal : for cell and molecular biology
Spartina alterniflora is a halophyte that can survive in high-salinity environments, and it is phylogenetically close to important cereal crops, such as maize and rice. It is of scientific interest to understand why S. alterniflora can live under suc...

A novel strategy to uncover specific GO terms/phosphorylation pathways in phosphoproteomic data in Arabidopsis thaliana.

BMC plant biology
BACKGROUND: Proteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to ...

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.

Scientific reports
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust re...

How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data.

International journal of molecular sciences
The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F population were genotype...