Computational Intelligence in Epigenetics, Cellular and Mitochondrial Functions

This reference text analyzes the integrated model of artificial intelligence, deep learning, and cloud computing for various bioinformatic problems including gene signature discovery, rare cell detection, disease classification, and rare disease detection.

This reference text analyzes the integrated model of artificial intelligence, deep learning, and cloud computing for various bioinformatic problems including gene signature discovery, rare cell detection, disease classification, and rare disease detection. It discusses biomedical image processing, and deep learning integrated tools for stage detection of critical diseases like cancer. This book:Presents a comparative study of gradient boosted classifier and deep learning classifier for disease classification of multi-omics sequencing data. Showcases biomarker discovery and differential expression analysis in Single-cell RNA sequencing data through the integrated model of data imputation, feature selection, and cell clustering. Explains artificial intelligence, machine learning, and integrated models for DNA methylation and gene expression data analysis, and disease classification. Covers the fundamentals of artificial intelligence, machine learning, deep learning, and cloud computing tools in bioinformatics. Explores the rare cell detection, and transcriptomic analysis in single-cell RNA sequencing. It is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.