- 320 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
About this book
Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences.
- Embraces the potential that deep learning holds for understanding genome biology
- Encourages further advances in this area, extending to all aspects of genomics research
- Provides Deep Learning algorithms in genetic and genomic research
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Deep Learning in Genetics and Genomics by Khalid Raza in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Molecular Biology. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Front Cover
- Deep Learning in Genetics and Genomics
- Deep Learning in Genetics and GenomicsVolume 2: Advanced Applications
- Copyright
- Dedication
- Contents
- Contributors
- Contributors
- About the editor
- Preface
- Acknowledgments
- About the book
- About the book
- 1 - Deep learning in predictive medicine exemplified by AI-mediated flu surveillance in USA
- 2 - Toward equitable precision medicine: Investigating the transferability of deep learning models in clinical gene ...
- 3 - Deep learning insights into transcriptomics and gene expression patterns analysis
- 4 - Role of artificial intelligence in clinical cancer genomics for oncology
- 5 - Deep learning approaches for interpreting Non-coding regions in Ovarian cancer
- 6 - Advancements in artificial intelligence-driven spatial transcriptomics: Decoding cellular complexity
- 7 - Advancements in clinical decision support through deep learning approaches in genetic diagnostics
- 8 - Neural architectures for genomic understanding: Deep dive into epigenome and chromatin structure
- 9 - Deep learning in personalized genomics and gene editing
- 10 - Deep learning–based model for prediction of prognostic genes of breast cancer using transcriptomic data
- 11 - Genomic image analysis: Bridging genomics and advanced imaging
- 12 - Qualitative study on steganography of genomic image data for secure data transmission using deep learning models
- 13 - Generative artificial intelligence in genetics: A comprehensive review
- 14 - Integrating computational biology and multiomics data for precision medicine in personalized cancer treatment
- 15 - Deep generative models in utilitarian and metamorphic genomics—Intellectual benefits
- 16 - Bridging the gap: Understanding genetic discoveries through explainable artificial intelligence
- 17 - Explainable Artificial Intelligence in genetics: A case study
- 18 - Deep learning in predicting genetic disorders: A case study of diabetic kidney disease
- 19 - Artificial intelligence and deep learning in single-cell omics data analysis: A case study
- 20 - Deep learning for network building and analysis of biological networks: A case study
- 21 - Transformer networks and autoencoders in genomics and genetic data interpretation: A case study
- Index
- Back Cover