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.
Item Type: | Book Section |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science |
Subjects: | 000 Computer science, information and general works > 004 Data processing computer science |
ISBN: | 978-3-319-43948-8; 978-3-319-43949-5 |
Place of Publication: | Cham |
Language: | English |
Item ID: | 47350 |
Date Deposited: | 27. Apr 2018, 08:12 |
Last Modified: | 13. Aug 2024, 12:54 |