國立虎尾科技大學 |

Machine learning in single-cell RNA-seq data analysis

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning in single-cell RNA-seq data analysis/ by Khalid Raza.
作者:	Raza, Khalid.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xviii, 88 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Data Analysis and Big Data. -
電子資源:	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:

1366136
Data Analysis and Big Data.

LC Class. No.: QP625.N89

Dewey Class. No.: 572.8833

Machine learning in single-cell RNA-seq data analysis
LDR:02424nam a22003495a 4500 001 1134577
003 DE-He213
005 20240903151938.0
006 m d
007 cr nn 008maaau
008 241213s2024 si s 0 eng d
020 $a 9789819767038 $q (electronic bk.)
020 $a 9789819767021 $q (paper)
024 7 $a 10.1007/978-981-97-6703-8 $2 doi
035 $a 978-981-97-6703-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QP625.N89
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 572.8833 $2 23
090 $a QP625.N89 $b R278 2024
100 1 $a Raza, Khalid. $e editor. $1 https://orcid.org/0000-0002-3646-6828 $3 1354511
245 1 0 $a Machine learning in single-cell RNA-seq data analysis $h [electronic resource] / $c by Khalid Raza.
260 $a Singapore : $c 2024. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xviii, 88 p. : $b ill. (some col.), digital ; $c 24 cm.
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in computational intelligence, $x 2625-3712
505 0 $a 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.
520 $a 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.
650 2 4 $a Data Analysis and Big Data. $3 1366136
650 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Artificial Intelligence. $3 646849
650 0 $a Machine learning. $3 561253
650 0 $a Nucleotide sequence $x Data processing. $3 670311
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computational intelligence. $3 1415104
856 4 0 $u https://doi.org/10.1007/978-981-97-6703-8
950 $a Biomedical and Life Sciences (SpringerNature-11642)