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Sinopsis
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.
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Acerca del autor
Dr. Khalid Raza is an Associate Professor at the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi, India. He also served as a ICCR Chair Visiting Professor at the Faculty of Computer & Information Sciences, Ain Shams University, Cairo, Egypt academic session 2017-2018. Dr. Raza has over 13 years of teaching and research experience at undergraduate, postgraduate and doctoral levels. He has contributed over 140 peer-reviewed articles in refereed international journals, conference proceedings, and as book chapters. Dr. Raza has authored/edited 10 Books published by reputed publishers like Springer-Nature, Elsevier, and CRC Press. Dr. Raza is an Academic Editor of PeerJ Computer Science International Journal, Guest Editor of the Natural Product Communications (SAGE), and Mini-Reviews in Medicinal Chemistry (Bentham Science). He has an active collaboration with the scientists from leading institutions of India and abroad. He is an Honorary Research Fellow of INIT International University, Malaysia. Dr. Raza has been featured in the list of World's Top 2% Scientists released by Stanford University (USA) in collaboration with Elsevier for the two consecutive years 2021 and 2022.
De la contraportada
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.
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- EditorialSpringer
- Año de publicación2024
- ISBN 10 9819767024
- ISBN 13 9789819767021
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas108
- Contacto del fabricanteno disponible
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Machine Learning in Single-Cell RNA-seq Data Analysis
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Springer, Berlin, Springer Nature Singapore, Springer, 2024
ISBN 10: 9819767024
ISBN 13: 9789819767021
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 88 pp. Englisch. Nº de ref. del artículo: 9789819767021
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Machine Learning in Single-Cell RNA-seq Data Analysis (SpringerBriefs in Computational Intelligence)
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ISBN 10: 9819767024
ISBN 13: 9789819767021
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Machine Learning in Single-Cell RNA-seq Data Analysis
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ISBN 10: 9819767024
ISBN 13: 9789819767021
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9789819767021
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Machine Learning in Single-cell Rna-seq Data Analysis
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Machine Learning in Single-Cell RNA-seq Data Analysis
Publicado por
Springer Nature Singapore, Springer Nature Singapore Sep 2024, 2024
ISBN 10: 9819767024
ISBN 13: 9789819767021
Nuevo
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 108 pp. Englisch. Nº de ref. del artículo: 9789819767021
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