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@INBOOK{hauskrecht2006fundamentals,
  chapter = {Feature Selection and Dimensionality Reduction in Genomics and Proteomics},
  title = {Fundamentals of Data Mining in Genomics and Proteomics},
  publisher = {Springer},
  year = {2006},
  editor = {Berrar and Dubitzky and Granzow},
  author = {Milos Hauskrecht and Richard Pelikan and Michal Valko and James Lyons--Weiler},
  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.}
}