AI-Driven Plant Biotechnology

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, advancing the exploration of multidisciplinary knowledge in 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 published books in collaboration with international publishers, and he is handling book projects on diverse topics such as 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 2023, 2024, and 2025 by Elsevier and Stanford University. In 2025, Dr. Chen received the Springer Nature Editor of Distinction Award.

• AI Models in Plant Research: Fundamentals and Biotechnological Applications. AI Models for Advancing Plant Genetics and Genetic Engineering. AI Models for Advancing Plant Genome Editing. AI Models for Integrating Plant Multiple Omics. AI Models for Studying Plant Functional Genomics. AI Models for Studying Plant Proteomics. AI Models for Studying Plant Metabolomics. AI Models for Advancing Plant Phenotyping and Phenomics. AI Models for Studying Plant Epigenetics. AI Models for Advancing Plant Single-Cell Omics. AI Models for Plant Systems Biology. AI Models for Advancing Plant Tissue Culture and Bioreactors. AI Models for Advancing Secondary Metabolite Production. AI Models in Plant Bioinformatics and Computational Biology. AI Models for Plant Genomic Selection and Crop Breeding. AI Models for Advancing Plant Nanotechnology. AI Models in Plant Disease Management. AI Models for Advancing Plant Abiotic Stress Research. AI Models for Monitoring Plant Growth and Development. Ethical Considerations, Limitations and Challenges for AI Models in Plant Biotechnology.