9783330333864 - A Hybrid Gene Selection Techniques for Cancer Classification de Anidha, M.; Premalatha, K. (6 resultados)

Paperback. Condición: Brand New. 60 pages. 8.66x5.91x0.14 inches. In Stock.

Taschenbuch. Condición: Neu. A Hybrid Gene Selection Techniques for Cancer Classification | M. Anidha (u. a.) | Taschenbuch | 60 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330333864 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Due to the high dimensionality of microarray gene expression data, feature selection is an important task which improves the performance of the classification in the area of cancer diagnosis. In this article, innovative methods are proposed for selecting highly informative gene subsets of gene expression data that effectively classify the cancer data into tumorous and non-tumorous. A novel technique to identify signature biomarkers is also discussed. 60 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Anidha MM. Anidha is full-time scholar research scholar in Anna University Chennai. Her area of research includes Bioinformatics and Data mining.Dr. K. Premalatha is currently working as a Professor in Bannari Amman Institute of Te.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Due to the high dimensionality of microarray gene expression data, feature selection is an important task which improves the performance of the classification in the area of cancer diagnosis. In this article, innovative methods are proposed for selecting highly informative gene subsets of gene expression data that effectively classify the cancer data into tumorous and non-tumorous. A novel technique to identify signature biomarkers is also discussed.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Due to the high dimensionality of microarray gene expression data, feature selection is an important task which improves the performance of the classification in the area of cancer diagnosis. In this article, innovative methods are proposed for selecting highly informative gene subsets of gene expression data that effectively classify the cancer data into tumorous and non-tumorous. A novel technique to identify signature biomarkers is also discussed.