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Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

Raman, Sudhir and Fuchs, Thomas and Wild, Peter and Dahl, Edgar and Buhmann, Joachim and Roth, Volker. (2010) Infinite mixture-of-experts model for sparse survival regression with application to breast cancer. BMC Bioinformatics, Vol. 11, Suppl 8 , S8.

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Official URL: http://edoc.unibas.ch/dok/A5840197

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Abstract

BACKGROUND:We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox`s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.RESULTS:Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers.CONCLUSIONS:The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker and Shankar Raman, Sudhir
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:BioMed Central
ISSN:1471-2105
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:11 Oct 2012 15:31
Deposited On:11 Oct 2012 15:21

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