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Survival regression by data fusion

Marinka Zitnik and Blaz Zupan (2014) Survival regression by data fusion. Systems Biomedicine, 2 (3). pp. 49-55.

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    Any knowledge discovery could in principal benefit from the fusion of directly or even indirectly related data sources. In this paper we explore whether data fusion by simultaneous matrix factorization could be adapted for survival regression. We propose a new method that jointly infers latent data factors from a number of heterogeneous data sets and estimates regression coefficients of a survival model. We have applied the method to CAMDA 2014 large- scale Cancer Genomes Challenge and modeled survival time as a function of gene, protein and miRNA expression data, and data on methylated and mutated regions. We find that both joint inference of data factors and regression coefficients and data fusion procedure are crucial for performance. Our approach is substantially more accurate than the baseline Aalen’s additive model. Latent factors inferred by our approach could be mined further; for CAMDA challenge, we found that the most informative factors are related to known cancer processes.

    Item Type: Article
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Divisions: Faculty of Computer and Information Science > Bioinformatics Laboratory
    Item ID: 3201
    Date Deposited: 09 Oct 2015 21:50
    Last Modified: 09 Oct 2015 21:50
    URI: http://eprints.fri.uni-lj.si/id/eprint/3201

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