Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving fuzzy clustering of biological data by metric learning with side information
International Journal of Approximate Reasoning
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
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Signature learning from gene expression consists into selecting a subset of molecular markers which best correlate with prognosis. It can be cast as a feature selection problem. Here we use as optimality criterion the separation between survival curves of clusters induced by the selected features. We address some important problems in this fields such as developing an unbiased search procedure and significance analysis of a set of generated signatures. We apply the proposed procedure to the selection of gene signatures for Non Small Lung Cancer prognosis by using a real data-set.