Covariate-Related Structure Extraction from Paired Data

Zhou, Linfei; Georgii, Elisabeth; Plant, Claudia und Böhm, Christian (2016): Covariate-Related Structure Extraction from Paired Data. In: Renda, M. Elena (Hrsg.): 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, Bd. 9832. Cham: Springer. S. 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.

• Dokumententyp: Buchbeitrag
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-3-319-43948-8; 978-3-319-43949-5
• Ort: Cham
• Sprache: Englisch