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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 124.
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Aggiungi al carrello23 x 16 cm. Condizione: Gut. 280 Seiten.; Mit Abbildungen und graphischen Darstellungen. Original gelber Pappband mit Bibliotheksschild. Gut erhalten. Innen mit den üblichen Bibliotheksstempeln- und Einträgen, teils durchgestrichen, sonst sehr sauberes Exemplar. B10-04-04C|S 20 Altersfreigabe FSK ab 0 Jahre Sprache: Deutsch Gewicht in Gramm: 402.
Lingua: Inglese
Editore: Spektrum Akademischer Verlag Gmbh, 2016
ISBN 10: 3658143185 ISBN 13: 9783658143183
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 110 pages. 8.00x5.75x0.25 inches. In Stock.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, 2016
ISBN 10: 3658143185 ISBN 13: 9783658143183
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Machine Learning for Microbial Phenotype Prediction | Roman Feldbauer | Taschenbuch | BestMasters | xiii | Englisch | 2016 | Springer | EAN 9783658143183 | Verantwortliche Person für die EU: Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden Jun 2016, 2016
ISBN 10: 3658143185 ISBN 13: 9783658143183
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data. 124 pp. Englisch.
Da: Majestic Books, Hounslow, Regno Unito
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 124.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, 2016
ISBN 10: 3658143185 ISBN 13: 9783658143183
Da: moluna, Greven, Germania
EUR 48,37
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. XYZ|Publication in the field of Bioinformatic ScienceMicrobial Genotypes and Phenotypes.- Basics of Machine Learning.- Phenotype Prediction Packages.- A Model for Intracellular Lifestyle.This thesis presents a scalable, generic methodology.
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch.