书目名称 | Machine Learning in Single-Cell RNA-seq Data Analysis |
编辑 | Khalid Raza |
视频video | |
概述 | Covers basic concepts of single cell RNA-seq.Discusses integration of ML and scRNA-seq.Presents hands-on examples and case studies |
丛书名称 | SpringerBriefs in Applied Sciences and Technology |
图书封面 |  |
描述 | .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. . |
出版日期 | Book 2024 |
关键词 | Single Cell Data Analysis; Machine Learning in Genomics; Single Cell RNA-seq; Machine Learning in Singl |
版次 | 1 |
doi | https://doi.org/10.1007/978-981-97-6703-8 |
isbn_softcover | 978-981-97-6702-1 |
isbn_ebook | 978-981-97-6703-8Series ISSN 2191-530X Series E-ISSN 2191-5318 |
issn_series | 2191-530X |
copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |