Autorentext

Bipin Kumar Rai is a Professor in the Department of Computer Science & Engineering, Dayanand Sagar University, Bangalore. He has a PhD from Banasthali University, Rajasthan and M. Tech (CSE) from RGPV Bhopal & B. Tech (CSE) from UPTU Lucknow, India. Dr. Bipin Kumar Rai has 20+ years teaching experience in renowned institutions. He has been actively involved in various academic and administrative roles. His interests include Information security, ML and blockchain. He has published 50+ research papers in WoS/Scopus indexed journals/conferences, 5 books and 5 patents. He has worked as an Editor/Reviewer of several SCI/Scopus indexed journals and as a session chair/TPC member in several conferences.

Rupa Rani is an Assistant Professor in the Department of CSE at AKG Engineering College, Ghaziabad (India). She has a PhD in Computer Science & Engineering from Banasthali Vidyapith, Rajasthan, and has a MTech in Computer Engineering. She published 20+ research papers indexed in WoS/Scopus journals/conferences. Her research interests include data science, AI, ML and cyber security.

Gautam Kumar is an Associate Professor at Manipal University Jaipur, India with the School of Computer Science and Engineering. He has a PhD degree in CSE from Jaypee University of Information Technology, India, and has MTech from RTU, Rajasthan & B.E. from RGPV, Bhopal, MP. His research interests cover cryptography, information security, algorithms, design and analysis of algorithms. He has published 50+ journals/conferences papers indexed in SCI/Scopus, 3 published books and 5 published patents.

Klappentext

The book investigates future frontiers such as quantum computing and multi-omics integration, and presents a picture of the genetic environment to advance enhanced genomics, enabling more precise forecasts, and personalised therapies.

Inhalt

1. Introduction: Genomic Landscape & Deep Learning Rise
2. Unraveling Genetic Code: Basics & Deep Learning Role
3. Genomic Data Deluge: Handling Big Data with DL
4. Mining Biological Gold: DL in Disease, Drug, Evolution Insights
5. Navigating Genome: Neural Networks, Transfer Learning, Explainability
6. Ethical Considerations: Privacy, Fairness, Bias in Genomic Data
7. Blockchain Integration in Genomic Deep Learning: Securing, Validating, and Sharing Genetic Insights
8. Future Horizons: Multi-Omics, Quantum Computing, Collaboration
9. Case Studies: Real-world Apps, Lessons
10. Challenges & Opportunities: Technical Hurdles, Robust Models
11. Conclusion: Recap & Genomic Frontier Ahead