Covariate-Related Structure Extraction from Paired Data

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

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
• 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