Abstract
Recombinant DNA technology has opened up a novel field for the agricultural biotechnologists to explore and design custom-made crops with specific trait(s). In addition to enhancing yield, transgenic plants are designed to have enhanced defense against pathogens, remove toxic compounds from the soil, to be tolerant to environmental stresses, to be live bioreactors to produce pharmaceuticals, to have improved nutritional features reflecting changes in protein, oil or vitamin content or to have woody species with lower generation time. But all plants with novel characters come with a price. The objective of the article is to evaluate the utility of the transgenic plants with respect to their novel features, the economic implications, seed security, agricultural sustainability and food safety. The gene mapping program of Arabidopsis and rice gives rise to the ethical question of the commercial patentability of the bioengineered plants. The impact of this science on agriculture that is being faced by the world with special reference to the developing countries of Asia is being discussed.
Keywords: Arabidopsis, Agrobacterium, DISEASE, Bacillus thuringensis, (Cydia pomonella), (Ostrinia nubilalis), (Leptinotarsa decemlineata)., Photorhabdus lumnescens, Ustilago maydis, Halobacterium halobium
Call for Papers in Thematic Issues
Advanced AI Techniques in Big Genomic Data Analysis
The thematic issue on "Advanced AI Techniques in Big Genomic Data Analysis" aims to explore the cutting-edge methodologies and applications of artificial intelligence (AI) in the realm of genomic research, where vast amounts of data pose both challenges and opportunities. This issue will cover a broad spectrum of AI-driven strategies, ...read more
Genomic Insights into Oncology: Harnessing Machine Learning for Breakthroughs in Cancer Genomics.
This special issue aims to explore the cutting-edge intersection of genomics and oncology, with a strong emphasis on original data and experimental validation. While maintaining the focus on how machine learning and advanced data analysis techniques are revolutionizing our understanding and treatment of cancer, this issue will prioritize contributions that ...read more
Integrating Artificial Intelligence and Omics Approaches in Complex Diseases
Recent advancements in AI and omics methodologies have revolutionized the landscape of biomedical research, enabling us to extract valuable information from vast amounts of complex data. By combining AI algorithms with omics technologies such as genomics, proteomics, metabolomics, and transcriptomics, researchers can obtain a more comprehensive and multi-dimensional analysis of ...read more
Integrating Machine Learning with Genome Science: Pioneering Developments and Future Directions
Integrating machine learning (ML) with genome science is driving transformative advancements in fields such as genomics, personalized medicine, and drug discovery. Genomic data is vast and complex, making traditional analysis methods inadequate for uncovering deep insights. Machine learning, particularly deep learning models like convolutional neural networks (CNNs) and recurrent neural ...read more
Related Journals
Related Books
Mind Maps in Clinical Biochemistry
A Treatise on Ecological Science
From SARS-CoV to MARS-CoV
Microbiology for ICAR NET: A Comprehensive Exam Preparation Guide
Decolorization by Thanatephorus Cucumeris Dec 1
Industrial Applications of Soil Microbes
The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming
Handbook of Integrated Weed Management for Major Field Crops
Practical Biochemistry
Anticancer Drugs Sourced from Marine Life
9