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  • 1
    In: Biometrical Journal, March 2014, Vol.56(2), pp.287-306
    Description: Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step toward a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Most of these methods focus on classification problems, that is learn a model from data that discriminates patients into distinct clinical groups. Far less has been published on approaches that predict a patient's event risk. In this paper, we investigate eight methods that integrate network information into multivariable Cox proportional hazard models for risk prediction in breast cancer. We compare the prediction performance of our tested algorithms via cross‐validation as well as across different datasets. In addition, we highlight the stability and interpretability of obtained gene signatures. In conclusion, we find GeneRank‐based filtering to be a simple, computationally cheap and highly predictive technique to integrate network information into event time prediction models. Signatures derived via this method are highly reproducible.
    Keywords: Biomarker ; Cox Regression ; Gene Signature ; Network Information
    ISSN: 0323-3847
    E-ISSN: 1521-4036
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  • 2
    Language: English
    In: Biometrical Journal, April 2009, Vol.51(2), pp.304-323
    Description: Targeted gene perturbations have become a major tool to gain insight into complex cellular processes. In combination with the measurement of downstream effects DNA microarrays, this approach can be used to gain insight into signaling pathways. were first introduced by Markowetz . as a probabilistic method to reverse engineer signaling cascades based on the nested structure of downstream perturbation effects. The basic framework was substantially extended later on by Fröhlich ., Markowetz ., and Tresch and Markowetz. In this paper, we present a review of the complete methodology with a detailed comparison of so far proposed algorithms on a qualitative and quantitative level. As an application, we present results on estimating the signaling network between 13 genes in the ER‐α pathway of human MCF‐7 breast cancer cells. Comparison with the literature shows a substantial overlap.
    Keywords: Nested Effects Models ; Perturbation Data ; Signaling Pathway Inference
    ISSN: 0323-3847
    E-ISSN: 1521-4036
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