Fayaz, U and Wazat, S A and Aminul Islam, A K M (2026) AI-Assisted Genomic and Epigenomic Modulations in Plant Stress Adaptation. In: Plant Genomic Prediction AI-Enhanced Models for Plant Genomics and Crop Improvement. CABI, Oxfordshire, UK, pp. 352-368. ISBN 9781836991304
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Plants utilize both genomic and epigenomic modulations to adapt to stresses due to environmental conditions. Genomic changes involve alteration of the DNA sequence itself and change gene expression. While epigenomic changes involve DNA methylation and histone modifications, they affect gene expression without changing DNA sequences. The epigenomic mechanisms play a crucial role in how plants respond to or adapt to various stresses, including biotic and abiotic stresses such as diseases, drought, salinity, extreme heat, etc. Both genomic and epigenomic changes can work together to influence plant stress adaptation. For example, DNA methylation can change gene expression in response to drought; histone modification changes gene expression related to heat-shock proteins; also, DNA methylation and other epigenetic mechanisms can be involved in a plant’s response to salinity stress. Therefore, the integration of different omics approaches, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, is essential to understand plant adaptation to stress conditions. Artificial intelligence (AI) and machine learning (ML) algorithm helps to optimize omics technologies to steer more precise and effective prediction of a plant’s response to stresses. This chapter reviews different aspects of AI-assisted genomic and epigenomic modulations in plants for the improvement of their yield and resilience to climate change by understanding the molecular mechanisms and pathways of stress adaptation.
| Item Type: | Book Section |
|---|---|
| Divisions: | Global Research Program - Accelerated Crop Improvement |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Plant Stress Adaptation, AI-Assisted Genomics, DNA sequence, Artificial intelligence (AI), machine learning (ML) |
| Subjects: | Others > Genetics and Genomics |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 28 Oct 2026 06:17 |
| Last Modified: | 28 Oct 2026 06:17 |
| URI: | http://oar.icrisat.org/id/eprint/13668 |
| Acknowledgement: | UNSPECIFIED |
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