Computational Intelligence for Genomics Data
Published on 21. January 2025
300 pages
978-0-443-30081-3 (ISBN)
Description
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 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
More details
Other editions
Additional editions
Babita Pandey | Valentina Emilia Balas | Suman Lata Tripathi
Computational Intelligence for Genomics Data
Book
01/2025
Academic Press
€175.78
Shipment within 15-20 days
Content
Section 1: Introduction to biological data and analysis1.1 Genomic data1.2 Microarray analysis1.3 Hub gene selection1.4 Pathogenesis1.5 Expressive gene1.6 Gene reduction1.7 BiomarkersSection 2: Traditional Machine learning models for gene selection and classification2.1 Gene selection and liver disease classification using machine learning2.2 Gene selection and Diabetic kidney disease classification using machine learning2.3. Gene selection and neurodegenerative disease classification using machine learning2.4. Gene selection and neuromuscular disorder classification using machine learning2.5. Gene selection and cancer classification using machine learning2.6. Gene selection and disease classification using machine learningSection3: Deep learning models for gene selection and classification3.1 Gene selection and liver disease classification using deep learning3.2 Gene selection and Diabetic kidney disease classification using machine learning3.3. Gene selection and neurodegenerative disease classification using deep learning3.4. Gene selection and neuromuscular disorder classification using deep learning3.5. Gene selection and cancer classification using deep learning3.6. Gene selection and disease classification using deep learningSection 4: Gene selection and classification using Artificial intelligence-based optimization methods4.1 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 biology5.1. Use of LIME for diagnosis of disease5.2. Use of Shape for diagnosis of disease5.3. Quantitative graph theory for integrated omics dataSection 6: Applications of computational biology in healthcare6.1 Diagnosis of liver disorder6.2 Diagnosis of diabetic kidney disease6.3 Diagnosis of cancer6.4 Diagnosis of neurodegenerative disorder.6.5 Diagnosis of neuromuscular disorder6.6. Diagnosis of any other health disorder
