Self-organizing maps
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Self-organizing maps in mining gene expression data
Information Sciences: an International Journal
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
ACM SIGKDD Explorations Newsletter
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Microarrays nowadays have an almost ubiquitous presence in modern biological research The extent and versatility of the techniques that are available for analysis and interpretation of microarray experiments can be somehow bewildering to the interested biologists. Functional genomics involves the highthroughput analysis of large datasets of information derived from various biological experiments. Microarray technology makes this possible by monitoring the emitting fluorescence reflecting the expression levels of thousands of genes simultaneously, which are bound to the oligonucleotide probes specific for each of the putative gene sequences comprising the total genome of the investigated organism, under a particular condition.. This chapter is a brief overview of the basic concepts involved in a microarray experiment; and it aspires to provide a concise overview of key issues regarding the various steps of implementation of this promising experimental methodology. In this sense, the chapter gives a feeling for what the data actually represent, and will provide information on the various computational methods that one can employ to derive meaningful results from such experiments.