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.
1. Section 1: Introduction to biological data and analysis - p. xxii 2. Genomic data - p. 3 3. Extensive sequence analysis: revealing genomic knowledge throughout various domains - p. 17 4. Integrative omics analysis using graph theoretical framework - p. 31 5. Gene prioritization for cancer module identification - p. 45 6. Section 2: Traditional machine learning models for gene selection and classification - p. 56 7. Transforming disease classification: leveraging machine learning for Gene selection - p. 61 8. Study on breast cancer detection and classification using artificial intelligence techniques and sensors - p. 65 9. Deep learning techniques for gene selection and cancer classification: a detailed review - p. 79 10. Section 3: Deep learning models for gene selection and classification - p. 95 11. Chronic kidney disease: causes, treatment, management, and future scope - p. 99 12. Liver diseases: genetic factors, current challenges, and future directions - p. 113 13. Principal component analysis and optimization -based feature selection and extraction for gene expression microarray: a comparative study of classification of neuromuscular disorder - p. 123 14. Section 4: Gene selection and classification using artificial intelligence-based optimization methods - p. 131 15. Gene selection and liver classification using machine learning - p. 135 16. Machine learning algorithms for classification of cancer - p. 145 17. Fusion of autoencoder model for gene predication and RNA disease association - p. 153 18. Section 5: Explainable AI for computational biology - p. 163 19. Role of computational biology in the diagnosis of neurodegenerative disorders - p. 167 20. Artificial intelligence's applicability in cardiac imaging - p. 181 21. Section 6: Applications of computational biology in health - p. 191 22. Diagnosis of liver disorder - p. 199 23. Diagnosis of neuromuscular disorder - p. 225 24. Diagnosis of autism spectrum disorder—prediction and analysis of enhanced classification model based on machine learning - p. 241 25. Healthcare applications of computational genomics - p. 259 26. Diagnosis of liver disorder - p. 279 27. Index - p. 295
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.
1. Section 1: Introduction to biological data and analysis - p. xxii 2. Genomic data - p. 3 3. Extensive sequence analysis: revealing genomic knowledge throughout various domains - p. 17 4. Integrative omics analysis using graph theoretical framework - p. 31 5. Gene prioritization for cancer module identification - p. 45 6. Section 2: Traditional machine learning models for gene selection and classification - p. 56 7. Transforming disease classification: leveraging machine learning for Gene selection - p. 61 8. Study on breast cancer detection and classification using artificial intelligence techniques and sensors - p. 65 9. Deep learning techniques for gene selection and cancer classification: a detailed review - p. 79 10. Section 3: Deep learning models for gene selection and classification - p. 95 11. Chronic kidney disease: causes, treatment, management, and future scope - p. 99 12. Liver diseases: genetic factors, current challenges, and future directions - p. 113 13. Principal component analysis and optimization -based feature selection and extraction for gene expression microarray: a comparative study of classification of neuromuscular disorder - p. 123 14. Section 4: Gene selection and classification using artificial intelligence-based optimization methods - p. 131 15. Gene selection and liver classification using machine learning - p. 135 16. Machine learning algorithms for classification of cancer - p. 145 17. Fusion of autoencoder model for gene predication and RNA disease association - p. 153 18. Section 5: Explainable AI for computational biology - p. 163 19. Role of computational biology in the diagnosis of neurodegenerative disorders - p. 167 20. Artificial intelligence's applicability in cardiac imaging - p. 181 21. Section 6: Applications of computational biology in health - p. 191 22. Diagnosis of liver disorder - p. 199 23. Diagnosis of neuromuscular disorder - p. 225 24. Diagnosis of autism spectrum disorder—prediction and analysis of enhanced classification model based on machine learning - p. 241 25. Healthcare applications of computational genomics - p. 259 26. Diagnosis of liver disorder - p. 279 27. Index - p. 295
