From Genes to Algorithms: Navigating the Biotechnology Data Revolution
- English
- ePUB (mobile friendly)
- Available on iOS & Android
From Genes to Algorithms: Navigating the Biotechnology Data Revolution
About this book
Positioned at the crossroads of genomics, proteomics, artificial intelligence, and biomedical engineering, this book provides a roadmap for leveraging computational intelligence to address the complex challenges of modern life sciences, healthcare, and industrial biotechnology. Across twelve comprehensive chapters, the book lays the foundations for sequencing technologies, omics data, and the principles of biotechnology data management. It then transitions into the application of machine learning models ranging from neural networks to optimization frameworks to extract meaningful insights from large-scale biological datasets. Subsequently it addresses pressing challenges such as data noise, scalability, and ethical AI, while also highlighting algorithmic breakthroughs in pharmacogenomics, drug discovery, precision medicine, and synthetic biology. Case studies illustrate real-world applications, from CRISPR diagnostics and clinical trial optimization to agricultural genomics and biomedical engineering innovations. The closing chapters project the future trajectory of biotechnology, exploring quantum computing, federated learning, and secure data-sharing techniques. Key Features: Uncovers the revolutionary role of computational algorithms in biotechnology research and healthcare Explores the integration of AI, ML, and optimization methods in genomics, proteomics, and systems biology Analyzes real-world applications through case studies in pharmacogenomics, CRISPR, and agritech Provides practical insights into implementing secure, scalable, and ethical data solutions Gives an understanding future trends such as quantum computing and federated learning in biotech innovation.
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Information
Table of contents
- Welcome
- Table of Contents
- Title
- BENTHAM SCIENCE PUBLISHERS LTD.
- FOREWORD
- PREFACE
- List of Contributors
- Unraveling the Biotechnology Data Revolution: A Roadmap
- Machine Learning in Biotechnology: Current Applications and Future Prospects
- Next-generation Sequencing Technologies
- Challenges and Opportunities in Biotechnology Data
- An Analysis of Optimization Techniques to Explore the Possibilities in Brain-computer Interfaces: Mindful Machines
- Advancements in Landmine Detection: A Comprehensive Exploration of Ground-penetrating Radar Technology
- Artificial Intelligence-driven Multilayer Network Analysis in Systems Biology
- Pharmacogenomics: Tailoring Drug Therapies
- Secure Data Sharing in Cloud Environments Using Blowfish Algorithm
- Integrating Machine Learning with Genomic Data for Predictive Modeling
- Precision Medicine and Clinical Applications
- Case Studies on Advances in Biotechnology