Integrating Genomic and Environmental Data Using Machine Learning for Vernalization Response Prediction
Mehmood, T.
Show abstract
This research investigates the integration of genomic and environmental data using Random Forests to predict vernalization response in barley. Vernalization, the requirement of a prolonged period of cold to induce flowering, is a critical adaptive trait for temperate cereal crops. The study compiles a comprehensive dataset of barley genotypes, gene expression levels related to vernalization (e.g., VRN1, VRN2, and FT1 genes), and detailed environmental variables including temperature, photoperiod, soil moisture, and humidity. By employing a Random Forest algorithm, the research identifies key genetic and environmental factors that influence vernalization. The findings suggest that this machine learning approach effectively models the complex interactions between genotype and environment, providing insights for breeding climate-resilient barley varieties. This integrative approach not only enhances our understanding of the genetic basis of vernalization but also aids in the development of barley varieties with optimized flowering times for diverse climatic conditions.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Development of a model estimating root length density from root impacts on a soil profile in pearl millet (Pennisetum glaucum (L.) R. Br). Application to measure root system response to water stress in field conditions 95%
- New genotypic adaptability and stability analyses using Legendre polynomials and genotype-ideotype distances 95%
- Near-infrared spectroscopy outperforms genomic selection for predicting sugarcane feedstock quality traits 95%
Similar papers in this journal
- An integrative process-based model for biomass and yield estimation of hardneck garlic (Allium sativum) 95%
- Identification of QTL hotspots affecting agronomic traits and high-throughput vegetation indices in rainfed wheat 95%
- Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning 94%
Similar papers in this journal
- Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program 97%
- Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement 95%
- Genomic and Phenomic Prediction for Soybean Seed Yield, Protein, and Oil 94%
Similar papers in this journal
- Development Of A Soybean Maturity Prediction Model For Soybean Grown In African Environments 95%
- Post-GWAS Prioritization of Genome-Phenome Associations in Sorghum 95%
- Genome-Wide Association Studies and Genomic Selection for Grain Protein Content Stability in a Nested Association Mapping Population of Spring Wheat 94%
Similar papers in this journal
- Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios 95%
- Predicting flowering time using integrated morphophysiological and genomic data with machine learning models 94%
- Application of Pedimap -- a pedigree visualization tool -- to facilitate the decisioning of rice breeding in Sri Lanka 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.