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Machine learning in single-cell RNA-seq data analysis

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning in single-cell RNA-seq data analysis/ by Khalid Raza.
Author:	Raza, Khalid.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xviii, 88 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Nucleotide sequence - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-97-6703-8
ISBN:	9789819767038

Machine learning in single-cell RNA-seq data analysis
Raza, Khalid.

Machine learning in single-cell RNA-seq data analysis[electronic resource] /by Khalid Raza. - Singapore :Springer Nature Singapore :2024. - xviii, 88 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computational intelligence,2625-3712. - SpringerBriefs in computational intelligence..

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.

ISBN: 9789819767038

Standard No.: 10.1007/978-981-97-6703-8doiSubjects--Topical Terms:

670311
Nucleotide sequence
--Data processing.

LC Class. No.: QP625.N89

Dewey Class. No.: 572.8833

Machine learning in single-cell RNA-seq data analysis
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