An Algorithm for Finding Gene Signatures Supervised by Survival Time Data

  • Stefano M. Pagnotta
  • Michele Ceccarelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6881)

Abstract

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.

Keywords

Bayesian Information Criterion Gene Ranking Seed Gene Prognosis Group Feature Selection Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stefano M. Pagnotta
    • 1
  • Michele Ceccarelli
    • 1
    • 2
  1. 1.Department of ScienceUniversity of SannioBeneventoItaly
  2. 2.Bioinformatics CORE, BIOGEM s.c.a.r.l., Contrada CamporealeUniversity of SannioArianoItaly

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