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Computational Intelligence for Genomics Data
- 1st Edition - February 1, 2025
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 8 0 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 8 1 - 3
Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the develo… Read more
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Request a sales quoteComputational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.
- Provides comparative analysis of machine learning and deep learning methods in the analysis of genomic data, discussing major design challenges, best practices, pitfalls, and research potential
- Explores machine and deep learning techniques applied to dimensionality reduction, feature extraction, data selection, and their application in genomics
- Presents case studies of various diseases based on gene microarray expression data, including cancer, liver disorders, neuromuscular disorders, and neurodegenerative disorders
Graduate students, researchers, and professionals in the fields of artificial intelligence, computational modeling, data analysis, and bioinformatics
Section 1: Introduction to biological data and analysis
1.1 Genomic data
1.2 Microarray analysis
1.3 Hub gene selection
1.4 Pathogenesis
1.5 Expressive gene
1.6 Gene reduction
1.7 Biomarkers
Section 2: Traditional Machine learning models for gene selection and classification
2.1 Gene selection and liver disease classification using machine learning
2.2 Gene selection and Diabetic kidney disease classification using machine learning
2.3. Gene selection and neurodegenerative disease classification using machine learning
2.4. Gene selection and neuromuscular disorder classification using machine learning
2.5. Gene selection and cancer classification using machine learning
2.6. Gene selection and disease classification using machine learning
Section3: Deep learning models for gene selection and classification
3.1 Gene selection and liver disease classification using deep learning
3.2 Gene selection and Diabetic kidney disease classification using machine learning
3.3. Gene selection and neurodegenerative disease classification using deep learning
3.4. Gene selection and neuromuscular disorder classification using deep learning
3.5. Gene selection and cancer classification using deep learning
3.6. Gene selection and disease classification using deep learning
Section 4: Gene selection and classification using Artificial intelligence-based optimization methods
4.1 Gene selection and liver disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.2 Gene selection and Diabetic kidney disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.3. Gene selection and neurodegenerative disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.4. Gene selection and neuromuscular disorder classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.5 Gene selection and cancer classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
Section 5: Explainable AI for computational biology
5.1. Use of LIME for diagnosis of disease
5.2. Use of Shape for diagnosis of disease
5.3. Quantitative graph theory for integrated omics data
Section 6: Applications of computational biology in healthcare
6.1 Diagnosis of liver disorder
6.2 Diagnosis of diabetic kidney disease
6.3 Diagnosis of cancer
6.4 Diagnosis of neurodegenerative disorder.
6.5 Diagnosis of neuromuscular disorder
6.6. Diagnosis of any other health disorder
1.1 Genomic data
1.2 Microarray analysis
1.3 Hub gene selection
1.4 Pathogenesis
1.5 Expressive gene
1.6 Gene reduction
1.7 Biomarkers
Section 2: Traditional Machine learning models for gene selection and classification
2.1 Gene selection and liver disease classification using machine learning
2.2 Gene selection and Diabetic kidney disease classification using machine learning
2.3. Gene selection and neurodegenerative disease classification using machine learning
2.4. Gene selection and neuromuscular disorder classification using machine learning
2.5. Gene selection and cancer classification using machine learning
2.6. Gene selection and disease classification using machine learning
Section3: Deep learning models for gene selection and classification
3.1 Gene selection and liver disease classification using deep learning
3.2 Gene selection and Diabetic kidney disease classification using machine learning
3.3. Gene selection and neurodegenerative disease classification using deep learning
3.4. Gene selection and neuromuscular disorder classification using deep learning
3.5. Gene selection and cancer classification using deep learning
3.6. Gene selection and disease classification using deep learning
Section 4: Gene selection and classification using Artificial intelligence-based optimization methods
4.1 Gene selection and liver disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.2 Gene selection and Diabetic kidney disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.3. Gene selection and neurodegenerative disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.4. Gene selection and neuromuscular disorder classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.5 Gene selection and cancer classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
Section 5: Explainable AI for computational biology
5.1. Use of LIME for diagnosis of disease
5.2. Use of Shape for diagnosis of disease
5.3. Quantitative graph theory for integrated omics data
Section 6: Applications of computational biology in healthcare
6.1 Diagnosis of liver disorder
6.2 Diagnosis of diabetic kidney disease
6.3 Diagnosis of cancer
6.4 Diagnosis of neurodegenerative disorder.
6.5 Diagnosis of neuromuscular disorder
6.6. Diagnosis of any other health disorder
- No. of pages: 300
- Language: English
- Edition: 1
- Published: February 1, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443300806
- eBook ISBN: 9780443300813