9781032055756 - gene expression data analysis: a statistical and machine learning perspective di barah, pankaj; bhattacharyya, dhruba kumar; kalita, jugal kumar (17 risultati)
Gene Expression Data Analysis : A Statistical and Machine Learning Perspective
Barah, Pankaj; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
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Gene Expression Data Analysis : A Statistical and Machine Learning Perspective
Barah, Pankaj; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
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Gene Expression Data Analysis: A Statistical and Machine Learning Perspective
Barah, Pankaj; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
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Gene Expression Data Analysis : A Statistical and Machine Learning Perspective
Barah, Pankaj; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
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Gene Expression Data Analysis: A Statistical and Machine Learning Perspective
Barah, Pankaj; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
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Condizione: New. First edition Includes bibliographical references and index.
Gene Expression Data Analysis : A Statistical and Machine Learning Perspective
Barah, Pankaj; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Gene Expression Data Analysis: A Statistical and Machine Learning Perspective
Barah, Pankaj; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Gene Expression Data Analysis: A Statistical and Machine Learning Perspective
Barah, Pankaj/ Bhattacharyya, Dhruba Kumar/ Kalita, Jugal Kumar
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 378 pages. 9.18x6.12x9.21 inches. In Stock.
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Gene Expression Data Analysis | A Statistical and Machine Learning Perspective | Pankaj Barah (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | Chapman and Hall/CRC | EAN 9781032055756 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 Mün…chen, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
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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 -Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput te…chnologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge.Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data.Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systemsA systematic overview of the methods for generating gene expression dataBackground knowledge on statistical modeling and machine learning techniquesDetailed methodology of analyzing gene expression data with an example case studyClustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA dataA large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patternsSuitable for multidisciplinary researchers and practitioners in computer science and biological sciences 380 pp. Englisch.
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Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technol…ogies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge.Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data.Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systemsA systematic overview of the methods for generating gene expression dataBackground knowledge on statistical modeling and machine learning techniquesDetailed methodology of analyzing gene expression data with an example case studyClustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA dataA large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patternsSuitable for multidisciplinary researchers and practitioners in computer science and biological sciences.
