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Machine Learning and AI for Precision Plant Epigenetics [#1031086]
Machine Learning and AI for Precision Plant Epigenetics
by Jen-Tsung Chen
English | 2026 | ISBN: 1394380283 | 490 pages | True PDF | 8.25 MB
Harness artificial intelligence to develop stress-resilient crops for sustainable agriculture
Machine Learning and AI for Precision Plant Epigenetics demonstrates how to develop climate-resilient crops by integrating AI with RNA-based epigenetic technologies. Edited by Professor Jen-Tsung Chen, a leader in plant biotechnology, this volume integrates insightful contributions from experts around the world that discuss how ML and AI models can revolutionize plant breeding and crop improvement to ensure food security under changing environmental conditions.
The book explores applications across sixteen chapters, covering AI-driven epigenome engineering, CRISPR/Cas9-mediated precision editing, intelligent approaches to combat abiotic and biotic stresses, and AI-enabled RNA interference. It explores the use of AI models for studying non-coding RNAs, predicting plant epigenetic landscapes, unlocking heat stress memory mechanisms, and uncovering plant-microbiome interactions critical for productivity.
The book:
Integrates machine learning with RNA technologies to enhance epigenetic modifications through non-coding RNAs and refine gene silencing capabilities
Demonstrates AI-advanced CRISPR/Cas systems for precision genome engineering to develop crops with enhanced quality, yield, and stress resilience
Provides strategies for studying plant epigenetic landscapes under abiotic stress and developing intelligent priming systems against biotic threats
Features contributions from leading international researchers at prestigious institutions
Addresses ethical and regulatory considerations essential for responsible implementation of artificial intelligence in agricultural biotechnology and crop development
This essential resource is tailored for researchers in plant biology, stress physiology, crop breeding, computational biology, and bioinformatics. It offers a forward-looking perspective on developing sustainable agriculture systems that support global food security in an era of climate change and increasing environmental challenges.
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