Machine Learning in Single-Cell RNA-seq Data Analysis
Khalid Raza
Naturwissenschaften, Medizin, Informatik, Technik / Informatik
Beschreibung
This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets.
Kundenbewertungen
Cell Fate Prediction, Dimension Reduction in Single Cell, Single Cell Data Analysis, Gene Expression Analysis, Machine Learning in Single Cell Analysis, Single Cell RNA-seq, PCA in Single Cell, TSNE in Single Cell, Clustering in Single Cell, Machine Learning in Genomics, Single Cell Multi-Omics Integration, Trajectory Inference