9780470116623 - machine learning in bioinformatics (wiley series in bioinformatics) de zhang; rajapakse (22 resultados)
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Condición: New. pp. xviii + 456 Illus.
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 181,68
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Hardback. Condición: New. An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequence…s, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 172,66
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Condición: New. Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc. , have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. Series: Wiley Series in Bioinfor…matics. Num Pages: 456 pages, Illustrations. BIC Classification: PBW; PSAK; TJ. Category: (P) Professional & Vocational. Dimension: 237 x 164 x 27. Weight in Grams: 788. . 2008. 1st Edition. Hardcover. . . . .
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 189,76
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Condición: New. pp. xviii + 456.
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Librería: moluna, Greven, Alemaniamoluna
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EUR 151,41
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Gebunden. Condición: New. Yan-Qing Zhang, PhD, is an Associate Professor of Computer Science at the Georgia State University, Atlanta. His research interests include hybrid intelligent systems, neural networks, fuzzy logic, evolutionary computation, Yin-Yang computation, granular co.
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Hardcover. Condición: Brand New. 1st edition. 456 pages. 9.29x6.22x1.02 inches. In Stock.
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Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
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EUR 221,03
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Condición: New. Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc. , have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. Series: Wiley Series in Bioinfor…matics. Num Pages: 456 pages, Illustrations. BIC Classification: PBW; PSAK; TJ. Category: (P) Professional & Vocational. Dimension: 237 x 164 x 27. Weight in Grams: 788. . 2008. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 169,48
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Hardback. Condición: New. An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequence…s, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 186,89
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Buch. Condición: Neu. Neuware - An introduction to machine learning methods and their applications to problems in bioinformaticsMachine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of seq…uences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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Hardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 828.
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Hardcover. Condición: new. Hardcover. An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form… of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels. Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc. , have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 185,41
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Hardcover. Condición: Brand New. 1st edition. 456 pages. 9.29x6.22x1.02 inches. In Stock. This item is printed on demand.
