Plant Spatial Biology

Nyhet

Inbunden, Engelska, 2026

2 409 kr

Kommande

Systems biology and multi-omics techniques in plant sciences and crop development have advanced significantly in recent years allowing the analysis of molecular activity down to the resolution of single cells. This new discipline of plant spatial biology will lead to precision crop improvement by molecular profiling. This book aims to review the current state of spatial multi-omics and the digital biological tools that are expected to advance plant physiology, crop breeding and the development of new stress-resilient and high-yield crops. The book explores how spatial multi-omics in plants utilizes artificial intelligence (AI) including machine learning, deep learning, and generative models for modelling and simulating cellular activities at high-dimensional and temporal levels. The book is an ideal reference, providing the latest information on spatial multi-omics using plant high-throughput technology, advanced functional genomics, and AI models. It is essential reading for graduate students, teachers, researchers, and experts in the fields of plant sciences and plant breeding as well as agronomy and agriculture.

Produktinformation

Utgivningsdatum2026-07-31
Mått172 x 244 x undefined mm
FormatInbunden
SpråkEngelska
SerieCABI Biotechnology Series
Antal sidor384
FörlagCABI Publishing
ISBN9781836994558

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Dr. Jen-Tsung Chen is a professor of cell biology at the National University of Kaohsiung in Taiwan. He teaches cell biology, genomics, proteomics, plant physiology, and plant biotechnology. Dr. Chen's research interests include bioactive compounds, chromatography techniques, plant molecular biology, plant biotechnology, bioinformatics, and systems pharmacology. He is an active editor of academic books and international journals to advance the exploration of multidisciplinary knowledge involving plant physiology, plant biotechnology, nanotechnology, materials science, ethnopharmacology, and systems biology. He serves as an associate editor, editorial board member, and guest editor in reputed journals. Dr. Chen has published books in collaboration with international publishers and is currently handling book projects on diverse topics, including AI technologies, drug discovery, drug development, herbal medicine, medicinal biotechnology, nanotechnology, bioengineering, plant functional genomics, plant speed breeding, epigenetics, functional RNAs, and CRISPR-based genome editing. Dr. Chen is a productive author in academic publications and was recognized as one of the World's Top 2% Scientists between 2023 to 2025 by Elsevier and Stanford University. Dr. Chen received the Springer Nature Editor of Distinction Award in 2025.

1: Introduction to Plant Multiple Omics: Technical Advancement and Current Applications 2: Spatial Omics in Plants: Current Achievements, Challenges and Future Directions 3: Plant High-Throughput Transcriptomics: Fundamentals and Advances 4: Machine Learning-Enhanced Plant Functional Genomics 5: Artificial Intelligence Models for High-Dimensional and Digital Biology in Plants 6: Machine Learning-Driven Plant Spatial and Multiple Omics 7: Mass Spectrometry Imaging for Plant Spatial Metabolomics 8: Plant Regulatory Networks Uncovered by Spatial and Multiple Omics Tools 9: Spatial and Multi-Omics Approaches for Exploring Plant Evolutionary and Developmental Biology 10: Plant-Microbe Interactions Studied by Spatial and Multiple Omics Tools 11: Spatial and Multiple Omics in Horticultural Plants: Advances and Prospects 12: Spatial and Multiple Omics Approaches for Advancing Climate-Smart Crop Breeding 13: Omics Tools in Horticultural Research and Recent Applications 14: Spatial and Multiple Omics Approaches for Pest and Disease Management: Opportunities and Challenges 15: Single-Cell and Spatial Multi-Omics to Dissect Regulatory Roles of Plant Functional RNAs under Stress Conditions 16: Structural and Functional Analysis of Plant Non-Coding RNAs: Highlighting the Advances in RNA Structures 17: Spatial and Multiple Omics in Medicinal Plants: Advances and Prospects