Computational Intelligence for Genomics Data presents a comprehensive 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. The book includes the design, algorithms and simulations on MATLAB and Python for the larger prediction models. It also explores the possibilities of software and hardware-based applications and devices for genomic disease…mehr
Computational Intelligence for Genomics Data presents a comprehensive 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. The book includes the design, algorithms and simulations on MATLAB and Python for the larger prediction models. It also explores the possibilities of software and hardware-based applications and devices for genomic disease prediction models by providing case studies and multiple examples. This book will be a helpful resource for researchers, graduate students and professional engineers who are developing new data analysis techniques and prediction models for the analysis of genomics data.
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
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
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