03291nam a22005415i 4500 978-981-97-6703-8 DE-He213 20251030112933.0 cr nn 008mamaa 240902s2024 si | s |||| 0|eng d 9789819767038 978-981-97-6703-8 10.1007/978-981-97-6703-8 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Raza, Khalid. author. aut http://id.loc.gov/vocabulary/relators/aut 70979 Machine Learning in Single-Cell RNA-seq Data Analysis [electronic resource] / by Khalid Raza. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVIII, 88 p. 25 illus., 16 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computational Intelligence, 2625-3712 Chapter 1. Introduction to Single-Cell RNA-seq Data Analysis -- Chapter 2. Preprocessing and Quality Control -- Chapter 3. Dimensionality Reduction and Clustering -- Chapter 4. Differential Expression Analysis -- Chapter 5. Trajectory Inference and Cell Fate Prediction -- Chapter 6. Emerging Topics and Future Directions. 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. . Artificial intelligence. 58441 Machine learning. 55059 Quantitative research. Artificial Intelligence. 58441 Machine Learning. 55059 Data Analysis and Big Data. SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819767021 Printed edition: 9789819767045 SpringerBriefs in Computational Intelligence, 2625-3712 74073 https://doi.org/10.1007/978-981-97-6703-8 ZDB-2-SBL ZDB-2-SXB 63044 63044