Feature Selection and Dimensionality Reduction in Genomics and Proteomics - Archive ouverte HAL Access content directly
Book Sections Year : 2006

Feature Selection and Dimensionality Reduction in Genomics and Proteomics

(1) , (2) , (1, 3) , (4)
1
2
3
4

Abstract

Finding reliable, meaningful patterns in data with high numbers of attributes can be extremely difficult. Feature selection helps us to decide what attributes or combination of attributes are most important for finding these patterns. In this chapter, we study feature selection methods for building classification models from high-throughput genomic (microarray) and proteomic (mass spectrometry) data sets. Thousands of feature candidates must be analyzed, compared and combined in such data sets. We describe the basics of four different approaches used for feature selection and illustrate their effects on an MS cancer proteomic data set. The closing discussion provides assistance in performing an analysis in high-dimensional genomic and proteomic data.
Fichier principal
Vignette du fichier
chapter-Hauskrecht.pdf (210.5 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00643496 , version 1 (29-11-2011)

Identifiers

Cite

Milos Hauskrecht, Richard Pelikan, Michal Valko, James Lyons-Weiler. Feature Selection and Dimensionality Reduction in Genomics and Proteomics. Werner Dubitzky, Martin Granzow and Daniel Berrar. Fundamentals of Data Mining in Genomics and Proteomics, Springer, pp.149-172, 2006, ⟨10.1007/978-0-387-47509-7⟩. ⟨hal-00643496⟩
340 View
1012 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More