Computational Intelligence for Genomics Data

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.

• Provides comparative analysis of machine learning and deep learning methods in the analysis of genomic data, discussing the 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