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Bioinformatics 2008 24(16):i248-i253; doi:10.1093/bioinformatics/btn265
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data

Louis-Philippe Kronek 1,* and Anupama Reddy 2

1G-SCOP, Grenoble-Science Conception Organization and Production, 46, Avenue Viallet 38031, Grenoble, France and 2RUTCOR, Rutgers Center for Operations Research, 640 Bartholomew Rd., Piscataway, NJ 08854, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Survival analysis involves predicting the time to event for patients in a dataset, based on a set of recorded attributes. In this study we focus on right-censored survival problems. Detecting high-degree interactions for the estimation of survival probability is a challenging problem in survival analysis from the statistical perspective.

Results: We propose a new methodology, Logical Analysis of Survival Data (LASD), to identify interactions between variables (survival patterns) without any prior hypotheses. Using these set of patterns, we predict survival distributions for each observation. To evaluate LASD we select two publicly available datasets: a lung adenocarcinoma dataset (gene-expression profiles) and the other a breast cancer dataset (clinical profiles). The performance of LASD when compared with survival decision trees improves the cross-validation accuracy by 18% for the gene-expression dataset, and by 2% for the clinical dataset.

Availability: Executable codes will be provided upon request.

Contact: louis-philippe.kronek{at}g-scop.fr; areddy{at}rutcor.rutgers.edu


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