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Covariate-related structure extraction from paired data.
Lect. Notes Comput. Sc. 9832, 151-162 (2016)
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.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 9832,
Seiten: 151-162
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Research Unit Environmental Simulation (EUS)