🧬 Research Focus
Research focuses on developing AI- and machine-learning-driven computational genomics approaches for genomic prediction and trait discovery in soybean. The work integrates large-scale genomic data, SNP analysis, genome-wide association studies and predictive machine-learning models to identify genotype–trait associations and candidate genomic regions. Scalable computational and high-performance-computing techniques are explored to efficiently process large genomic datasets. The expected outcome is an intelligent computational framework that supports accurate trait prediction, accelerates candidate-gene discovery, and contributes to data-driven soybean breeding and crop improvement.
Research Details
AI-Driven Computational Genomics for Genomic Prediction and Accelerated Crop Improvement in Soybean
Genomic Prediction
Predictive machine-learning models for trait prediction and candidate-gene discovery in soybean.
Graph Neural Networks
GNN-based modelling of genomic relationships and SNP interactions for accurate prediction.
Genome-Wide Association Studies
GWAS and SNP analysis to identify genotype–trait associations and candidate genomic regions.
Scalable HPC Genomics
High-performance computing for efficient processing of large-scale genomic datasets.
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