Infinite mixture-of-experts model for sparse survival regression with application to breast cancer
Fuchs, Thomas J.
Wild, Peter J.
Buhmann, Joachim M.
- Conference Paper
Rights / licenseCreative Commons Attribution 2.0 Generic
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 Show more
Journal / seriesBMC Bioinformatics
Pages / Article No.
SubjectWeibull Distribution; Lasso; Dirichlet Process; Markov Chain Monte Carlo Sampling; Baseline Hazard Function
Organisational unit03659 - Buhmann, Joachim M.
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