Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

Area of Science:

• Agricultural Science
• Genomics
• Computational Biology

Background:

• Crop production faces threats from diseases and environmental stress, causing significant global food losses.
• Genomic sequencing and phenotyping technologies are revolutionizing plant breeding by enabling precise identification of genetic traits.
• Machine learning (ML) is crucial for analyzing large datasets in plant genomics.

Purpose of the Study:

• To review advancements in next-generation sequencing and phenotyping for genomics-assisted breeding.
• To explore the application of ML in managing big data and developing predictive models for crop improvement.
• To identify genes associated with stress tolerance using a case study on microRNAs (miRNAs).

Main Methods:

• Review of recent progress in next-generation sequencing (NGS) technologies.
• Analysis of phenotyping technologies in the context of genomics-assisted breeding.
• Application of machine learning algorithms for big data analysis and predictive modeling.
• Case study utilizing microRNAs (miRNAs) for gene identification under stress conditions.

Main Results:

• Genomics-assisted breeding, leveraging NGS and phenotyping, enhances the identification of genetic variations.
• Machine learning effectively manages large-scale crop data, aiding in decision-making for breeding targets.
• The miRNA case study demonstrated the potential of ML in pinpointing genes related to stress responses.

Conclusions:

• Integrating advanced sequencing, phenotyping, and ML is vital for modern plant breeding.
• These technologies accelerate the discovery of genes controlling desirable crop traits, including stress tolerance.
• The findings support the use of ML-driven approaches to enhance crop resilience and food security.