Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications, the intersection of deep learning and genetics opens up new avenues for advancing our understanding of the genetic code, gene regulation, and the broader genomics landscape. The book not only covers the most up-to-date advancements in the field of deep learning in genetics and genomics, but also a wide spectrum of (sub) topics including medical and clinical genetics, predictive medicine, transcriptomic, and gene expression studies. In 21 chapters Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications describes how AI and DL have become increasingly useful in genetics and genomics research where both play a crucial role by accelerating research, improving the understanding of the human genome, and enabling personalized healthcare. From the fundamentals concepts and practical applications of deep learning algorithms to a wide range of challenging problems from genetics and genomics, Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications creates a better knowledge of the biological and genetics mechanisms behind disease illnesses and improves the forecasting abilities using the different methodologies described. This title offers a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning-based approaches in genetics and genomics, helping researchers process and interpret vast amounts of genetic data, identify patterns, and make discoveries that would be challenging or impossible using traditional methods.
Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.
Table of Contents
1. Basics of Genetics and Genomics2. Introduction to Deep Learning
3. Decoding DNA: Deep Learning’s Impact on Genomic Exploration
4. Deep learning for Biological Data analysis
5. AI and Deep Learning in Cancer Genomics
6. Unravelling the Recent Developments in Applications and Challenges of AI in Cancer Biology: An overview
7. Unlocking the Potential of Deep Learning for Oncological Sequence Analysis: A Review
8. Deep Learning in Medical Genetics: A Review
9. Navigating the Genomic Landscape: A Deep Dive into Clinical Genetics with Deep Learning
10. Advancing Clinical Genomics: Bridging the Gap between Deep Learning Models and Interpretability for Improved Decision Support
11. Deep Learning in Clinical Genomics-based Diagnosis of Cancers
12. Predictive Precision: Unleashing Deep Learning in Comprehensive Medical Diagnosis and Treatment
13. Deep Learning in Predictive Medicine: Current State-of-the-art
14. Applications of AI in Cancer Genomics: A Way toward Intelligent Decision Systems in Healthcare
15. The Role of Deep Learning in Drug Discovery
16. Deep Learning in Drug Discovery: Revolutionizing the Path to New Medicines
17. Deep Learning Application in Genomics-based Toxicology Assessment
18. The Revolutionary Impact of Deep Learning in Transcriptomics and Gene Expression Analysis: A Genomic Paradigm Shift
19. Machine Learning Applications in Biological Network Analysis: Current State-of-the-Art
20. Data-driven Genomics: A Triad of Big Data, Cloud, and IoT in Genomics Research
21. Deep Learning in Variant Detection and Annotation
Authors
Khalid Raza Department of Computer Science, Jamia Millia Islamia (Central University), Jamia Nagar, New Delhi, India.Dr. Khalid Raza is working as an Associate Professor at the Department of Computer Science, Jamia Millia Islamia, New Delhi. Earlier he worked as an "ICCR Chair Professor� at Ain Shams University, Cairo, Egypt. He has many years of teaching & research experiments in the field of Translational Bioinformatics and Computational Intelligence. He has contributed over 120 research articles in reputed Journals and Edited Books. Dr. Raza has authored/edited dozens of books published by reputed publishers. Dr. Raza is an Academic Editor of PeerJ Computer Science International Journal, and Guest Editor of the Journal Natural Product Communications. He has an active collaboration with the scientists from leading institutions of India and abroad. Recently, Dr. Raza has been featured in the list of Top 2% Scientists released by Stanford University (USA) in collaboration with Elsevier. His research interest lies in Machine Learning and its applications in Bioinformatics and Health-informatics.
