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.
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Pagnotta, S.M., Ceccarelli, M. (2011). An Algorithm for Finding Gene Signatures Supervised by Survival Time Data. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23851-2_58
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DOI: https://doi.org/10.1007/978-3-642-23851-2_58
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