Zhou, Linfei; Georgii, Elisabeth; Plant, Claudia; Böhm, Christian
(2016):
Covariate-Related Structure Extraction from Paired Data.
In: Renda, M. Elena (ed.) :
Information Technology in Bio- and Medical Informatics: 7th International Conference, ITBAM 2016, Porto, Portugal, September 5-8, 2016, Proceedings. Information Systems and Applications, incl. Internet/Web, and HCI, Vol. 9832. Cham: Springer. pp. 151-162
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Full text not available from 'Open Access LMU'.
Abstract
In the biological domain, it is more and more common to apply several high-throughput technologies to the same set of samples. We propose a Covariate-Related Structure Extraction approach (CRSE) that explores relationships between different types of high-dimensional molecular data (views) in the context of sample covariate information from the experimental design, for example class membership. Real-world data analysis with an initial pipeline implementation of CRSE shows that the proposed approach successfully captures cross-view structures underlying multiple biologically relevant classification schemes, allowing to predict class labels to unseen examples from either view or across views.