Chapter: Resampling Strategies for Model Assessment and Selection

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Home > Fundamentals of Data Mining in Genomics and Proteomics > Resampling Strategies for Model Assessment and Selection

Fundamentals of Data Mining in Genomics and Proteomics |

Werner Dubitzky; Martin Granzow; Daniel P. Berrar

Springer US

Fundamentals of Data Mining in Genomics and Proteomics

Resampling Strategies for Model Assessment and Selection

Chapter Info

Authors

Richard Simon

ARI Id

1664087630720_284501

Access

Not Available Free

Pages

173-186

DOI

10.1007/978-0-387-47509-7_8

Chapter URL

https://rd.springer.com/chapter/10.1007/978-0-387-47509-7_8

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Chapters/Headings	Author(s)	Pages	Info
Front Matter		i-xxii
Introduction to Genomic and Proteomic Data Analysis	Daniel Berrar; Martin Granzow; Werner Dubitzky	1-37
Design Principles for Microarray Investigations	Kathleen F. Kerr	39-49
Pre-Processing DNA Microarray Data	Benjamin M. Bolstad	51-78
Pre-Processing Mass Spectrometry Data	Kevin R. Coombes; Keith A. Baggerly; Jeffrey S. Morris	79-102
Visualization in Genomics and Proteomics	Xiaochun Li; Jaroslaw Harezlak	103-122
Clustering — Class Discovery in the Post-Genomic Era	Joaquín Dopazo	123-148
Feature Selection and Dimensionality Reduction in Genomics and Proteomics	Milos Hauskrecht; Richard Pelikan; Michal Valko; James Lyons-Weiler	149-172
Resampling Strategies for Model Assessment and Selection	Richard Simon	173-186
Classification of Genomic and Proteomic Data Using Support Vector Machines	Peter Johansson; Markus Ringnér	187-202
Networks in Cell Biology	Carlos Rodríguez-Caso; Ricard V. Solé	203-226
Identifying Important Explanatory Variables for Time-Varying Outcomes	Oliver Bembom; Maya L. Petersen; Mark J. van der Laan	227-249
Text Mining in Genomics and Proteomics	Robert Hoffmann	251-274
Back Matter		275-281
Chapters/Headings	Author(s)	Pages	Info

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