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Deep learning in genetics and genomics. Volume 1, Foundations and introductory applications /

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 adva...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Raza, Khalid (Associate professor of computer science) (Editor)
Format: eBook
Language:English
Published: London : Academic Press, [2025]
Subjects:
Genetics > Data processing.
Genomics > Data processing.
Artificial intelligence > Biological applications.
Deep learning (Machine learning)
Génétique > Informatique.
Génomique > Informatique.
Intelligence artificielle > Applications biologiques.
Apprentissage profond.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Deep Learning in Genetics and Genomics
  • Deep Learning in Genetics and GenomicsVolume 1: Foundations and Introductory Applications
  • Copyright
  • Dedication
  • Contents
  • Contributors
  • Contributors
  • About the editor
  • Preface
  • Acknowledgments
  • About the book
  • About the book
  • 1
  • Basics of genetics and genomics
  • 1. Introduction
  • 1.1 Structure of DNA
  • 1.2 Central dogma of biology
  • 1.3 DNA replication
  • 1.4 DNA transcription and protein synthesis
  • 2. Structure of genes and genomes
  • 2.1 Prokaryote genome structure
  • 2.2 Eukaryote genome structure
  • 3. Era of genetics
  • 3.1 The monohybrid cross
  • 3.2 The dihybrid cross
  • 3.3 Deviations from Mendel's laws
  • 4. Era of genomics
  • 4.1 Polymerase chain reaction (PCR)
  • 4.2 Early genomic experiments
  • 4.3 DNA sequencing
  • 4.4 High-throughput sequencing
  • 4.5 Next generation sequencing
  • 4.5.1 Pyrosequencing
  • 4.5.1.1 Data format
  • 4.5.2 Ion Torrent sequencing
  • 4.5.2.1 Data format
  • 4.5.3 Illumina sequencing by synthesis
  • 4.5.3.1 Data format
  • 4.6 Third generation sequencing
  • 4.6.1 Oxford Nanopore Technologies sequencing
  • 4.6.1.1 ONT data format
  • 4.6.2 PacBio sequencing
  • 4.6.2.1 Data format for PacBio sequencers
  • 5. Data analysis of genomic experiments-An overview
  • 6. Types of genomics experiments
  • 6.1 Structural genomics
  • 6.2 Functional genomics
  • 6.3 Applications of genomics
  • 7. Postgenomic era
  • 8. Conclusion
  • References
  • 2
  • Introduction to deep learning for genomics
  • 1. Introduction
  • 1.1 Specific contributions
  • 1.2 Background
  • 2. Fundamentals of deep learning
  • 2.1 Basic concepts
  • 2.2 Deep learning architectures
  • 2.3 Convolutional neural networks
  • 2.4 Feedforward neural networks
  • 2.5 Recurrent neural networks
  • 2.6 Long short-term memory networks
  • 2.7 Mathematical description of LSTM.
  • 2.8 Gated recurrent units
  • 2.9 Generative adversarial networks
  • 3. Applications of deep learning in genomics
  • 3.1 Sequence analysis
  • 3.2 Gene expression prediction
  • 3.3 Disease prediction and personalized medicine
  • 4. Advanced deep learning techniques in genomics
  • 4.1 Attention mechanisms and transformer models
  • 4.2 Generative adversarial networks
  • 4.3 Integration with emerging technologies
  • 5. Disadvantages and flaws of past machine learning technologies
  • 5.1 Key limitations
  • 5.2 Case study: GPT-3
  • 5.3 Improvements in GPT-4
  • 6. Challenges and solutions in deep learning for genomics
  • 6.1 Data challenges
  • 6.2 Model interpretability and explainability
  • 6.3 Computational and resource constraints
  • 7. Ethical and societal implications
  • 7.1 Ethical considerations
  • 7.2 Societal impact
  • 8. Future directions and emerging trends
  • 8.1 Trends in deep learning for genomics
  • 8.2 Research directions
  • 9. Conclusion
  • Declaration statements
  • References
  • 3
  • Foundations and applications of computational genomics
  • 1. Introduction
  • 2. Computational genomics and human genomes
  • 3. Computational tools and techniques
  • 4. Computational genomics and genetic diseases
  • 5. Computation genomics in application of analysis genetic diseases
  • 6. Case study on genetic disease analysis
  • 7. Limitations and future directions
  • 8. Conclusions
  • References
  • 4
  • Decoding DNA: Deep learning's impact on genomic exploration
  • 1. Introduction
  • 2. Deep learning methodologies for decoding DNA
  • 2.1 Neural networks and genomic pattern recognition
  • 2.2 Processing and analysing large genomic datasets
  • 2.3 Case studies of novel deep learning applications
  • 3. Unlocking the secrets of the genome
  • 3.1 Advances in gene sequencing analysis and insight
  • 3.2 Identifying critical sequences and genetic variants.
  • 3.3 Identifying intricate correlations and relationships within DNA
  • 3.4 Examples of recent breakthrough discoveries
  • 4. Improving prediction and diagnosis of disease
  • 4.1 Predicting disease risks from genomic data
  • 4.2 Enabling earlier and more accurate diagnoses
  • 4.3 Potential for precision and personalized medicine
  • 4.4 Promises and current limitations
  • 4.4.1 Promises
  • 4.4.2 Current limitations
  • 4.5 Future horizons: The path ahead for AI and genomics
  • 5. Conclusion
  • References
  • 5
  • AI and deep learning in cancer genomics
  • 1. Introduction
  • 2. Application of AI to cancer genomics data
  • 3. Machine learning for the classification of different cancer by using gene expression data
  • 4. Techniques of deep learning in the prognosis of cancer with genomics data
  • 5. Genomics-based AI in immunotherapy prediction
  • 6. AI and ML in genomic and precision medicine
  • 7. Challenges
  • 7.1 Technical challenges
  • 7.2 Ethical challenges
  • 8. Future directions
  • 9. Conclusion
  • List of abbreviations
  • References
  • 6
  • Unravelling the recent developments in applications and challenges of AI in cancer biology: An overview
  • 1. Introduction
  • 2. Prognosis
  • 3. Diagnosis
  • 4. Cancer treatment and management
  • 4.1 AI and chemotherapy, radiotherapy and immunotherapy
  • 4.2 Role of AI in cancer overtreatment and clinical decision support systems
  • 5. Hurdles for real-life deployment of AI
  • 5.1 Data, data privacy, and protection-related challenges
  • 6. Conclusion
  • References
  • 7
  • Unlocking the potential of deep learning for oncological sequence analysis: A review
  • 1. Introduction
  • 2. Essentials of oncological sequences
  • 2.1 Types of oncological sequences
  • 2.2 Challenges in oncological sequence analysis
  • 3. Overview of deep learning
  • 3.1 Deep learning architectures
  • 3.2 Convolutional neural networks.
  • 3.3 Recurrent neural networks
  • 3.4 Long short-term memory networks
  • 4. Deep learning applications in oncological sequence analysis
  • 4.1 Deep learning in genomics
  • 4.2 Deep learning in transcriptomics
  • 4.3 Deep learning in proteomics
  • 5. Challenges and future perspectives
  • 6. Conclusion
  • References
  • 8
  • Deep learning in medical genetics: A review
  • 1. Introduction
  • 2. Datasets
  • 2.1 Genomic benchmarks
  • 2.2 Genomics data lake
  • 2.3 Genomic data analysis
  • 3. Deep learning approaches
  • 3.1 Supervised deep learning methods
  • 3.2 Unsupervised deep learning methods
  • 3.3 Semi-supervised deep learning methods
  • 4. Future scope and challenges
  • 5. Conclusion
  • References
  • 9
  • Navigating the genomic landscape: A deep dive into clinical genetics with deep learning
  • 1. Introduction
  • 2. Deep learning
  • 2.1 Supervised learning
  • 2.2 Unsupervised learning
  • 2.3 Semi-supervised learning
  • 3. Deep learning tools in genomics
  • 4. Crucial role of deep learning in genomics
  • 4.1 Deep learning at DNA level
  • 4.1.1 Promoter
  • 4.1.2 Enhancer
  • 4.1.3 Noncoding region
  • 4.1.4 Interactions between genomic elements
  • 4.1.5 Other domains
  • 4.2 Deep learning at RNA level
  • 4.2.1 Splicing
  • 4.2.2 Noncoding RNA
  • 4.2.3 Messenger RNA
  • 4.2.4 Expression
  • 4.3 Deep learning at protein level
  • 4.3.1 Transcription factor
  • 4.3.2 RNA-specific binding proteins
  • 5. Deep learning in surgery
  • 6. Promising approach of deep learning in the detection of genetic disorders
  • 6.1 Neurology
  • 6.2 Fetal ultrasound
  • 6.3 Polycystic ovary syndrome
  • 6.4 Epilepsy
  • 6.5 Cardiovascular diseases
  • 7. Implications of deep learning in the medical health computational field
  • 7.1 Medical image
  • 7.2 Electronic health record
  • 7.3 Drug discovery
  • 7.4 Oncology
  • 8. Deep learning application in longitudinal datasets.
  • 9. Challenges of deep learning with their alternate solutions
  • 9.1 Training data
  • 9.2 Interpretability of data
  • 9.3 Uncertainty scaling
  • 10. Conclusion and future perspective
  • References
  • 10
  • Advancing clinical genomics: Bridging the gap between deep learning models and interpretability for improved d ...
  • 1. Introduction
  • 1.1 Background on clinical genomics
  • 1.2 The role of deep learning models in clinical genomics
  • 1.3 Challenges limiting adoption
  • 1.4 The critical need for interpretability and explainability
  • 1.5 Purpose of the study
  • 2. Overview of deep learning models in clinical genomics
  • 2.1 Current state of interpretability in deep learning models
  • 2.2 Existing challenges and limitations
  • 2.3 Recent advances in bridging the gap
  • 3. Methodology
  • 3.1 Search strategy
  • 3.2 Inclusion and exclusion criteria
  • 4. Techniques for improved interpretability
  • 4.1 Overview of proposed methodologies
  • 4.2 Detailed explanation of novel techniques
  • 4.3 Comparative analysis with existing approaches
  • 4.4 Evaluation metrics for interpretability
  • 5. Application of interpretability techniques in clinical settings
  • 5.1 Case studies and real-world applications
  • 5.2 Impact on healthcare professionals and clinical decision making
  • 5.3 Integration into routine clinical workflows
  • 5.4 Challenges and considerations for implementation
  • 6. Future directions and implications
  • 6.1 Potential benefits of enhanced interpretability
  • 6.2 Areas for further research and development
  • 6.3 Ethical and regulatory considerations
  • 6.4 Implications for advancing precision medicine
  • 7. Conclusion
  • 7.1 Summary of key findings
  • 7.2 Contributions to the field of clinical genomics
  • 7.3 Recommendations for future practice and policy
  • References
  • 11
  • Deep learning in clinical genomics-based cancer diagnosis.