9789819767021 - machine learning in single-cell rna-seq data analysis di raza, khalid (18 risultati)
Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Paperback. Condizione: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Machine Learning in Single-Cell RNA-seq Data Analysis (SpringerBriefs in Computational Intelligence)
Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Machine Learning in Single-Cell RNA-seq Data Analysis (SpringerBriefs in Computational Intelligence)
Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Machine Learning in Single-Cell RNA-seq Data Analysis (SpringerBriefs in Computational Intelligence)
Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Taschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Taschenbuch. Condizione: Neu. Machine Learning in Single-Cell RNA-seq Data Analysis | Khalid Raza | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | xviii | Englisch | 2024 | Springer | EAN 9789819767021 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]h…artmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer Verlag, Singapore, Singapore, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Paperback. Condizione: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Springer, Springer Sep 2024, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: 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. 108 pp. Englisch.
Machine Learning in Single-Cell RNA-seq Data Analysis (SpringerBriefs in Computational Intelligence)
Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Machine Learning in Single-Cell RNA-seq Data Analysis (SpringerBriefs in Computational Intelligence)
Lingua: Inglese
Editore: Springer, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
Lingua: Inglese
Editore: Springer, Springer Sep 2024, 2024
Serie: Libro 369 di 472 - SpringerBriefs in Applied Sciences and Technology
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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. Th…e 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 KG, Sachsenplatz 4-6, 1201 Wien 108 pp. Englisch.
