Journal of Applied and Integrative Biology
ISSN: 3139-1567 (Online)
School of Science, Uttar Pradesh Rajarshi Tandon Open University, Prayagraj, India
*Corresponding author: swatinatural@gmail.com
Received: 14 Nov 2025 | Accepted: 24 Dec 2025 | Published: 26 Dec 2025
The rapid expansion of high-throughput sequencing technologies has revolutionized plant genomics, generating complex datasets that require advanced analytical methods. Machine learning has emerged as a transformative approach for extracting meaningful biological insights from large datasets. In plant genome analysis, ML techniques are widely used for gene prediction, genome annotation, functional genomics, GWAS, epigenomics, and crop improvement. Despite significant advances, challenges related to data quality, genome complexity, model interpretability, and computational requirements remain. This review highlights recent advances and future perspectives in machine learning applications for plant genomics.
Machine learning, plant genomics, deep learning, GWAS, genome annotation, crop improvement
Plant genomics has advanced significantly with next-generation sequencing technologies, enabling deeper understanding of complex plant genomes. However, their large size, polyploidy, and repetitive nature make analysis challenging. Machine learning provides a data-driven framework to uncover patterns and relationships in genomic data, enhancing predictive accuracy and biological interpretation.
Machine learning techniques enable automated pattern recognition and predictive modeling in genomics. These include supervised learning, unsupervised learning, and deep learning approaches applied to genomic datasets.
ML methods are widely applied in gene prediction, functional annotation, GWAS, epigenomics, and crop improvement. These approaches improve prediction accuracy and enable analysis of high-dimensional biological data.
Advances in deep learning, transfer learning, and multi-omics integration have significantly improved genomic predictions and biological insights. Explainable AI is also emerging to enhance interpretability of ML models.
Challenges include limited labeled data, computational requirements, and lack of model interpretability. Future efforts should focus on improving data quality, developing interpretable models, and integrating computational predictions with experimental validation.
Machine learning is transforming plant genome analysis by enabling efficient interpretation of complex genomic data. Continued innovation will further enhance its role in sustainable agriculture and plant biology.
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