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

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

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