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Semantic ulti- lassifier ystems for the nalysis of ene xpression rofiles

Lausser, Ludwig; Schmid, Florian; Platzer, Matthias; Sillanpää, Mikko J.; Kestler, Hans A.

Abstract: The analysis of biomolecular data from high-throughput screens is typically characterized by the high dimensionality of the measured profiles. Development of diagnostic tools for this kind of data, such as gene expression profiles, is often coupled to an interest of users in obtaining interpretable and low-dimensional classification models; as this facilitates the generation of biological hypotheses on possible causes of a categorization. Purely data driven classification models are limited in this regard. These models only allow for interpreting the data in terms of marker combinations, often gene expression levels, and rarely bridge the gap to higher-level explanations such as molecular signaling pathways. Here, we incorporate into the classification process, additionally to the expression profile data, different data sources that functionally organize these individual gene expression measurements into groups. The members of such a group of measurements share a common property or characterize a more abstract biological concept. These feature subgroups are then used for the generation of individual classifiers. From the set of these classifiers, subsets are combined to a multi-classifier system. Analysing which individual classifiers, and thus which biological concepts such as pathways or ontology terms, are important for classification, make it possible to generate hypotheses about the distinguishing characteristics of the classes on a functional level.

Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator DOI: 10.5445/KSP/1000058747/09
ISSN: 2363-9881
URN: urn:nbn:de:swb:90-677674
KITopen ID: 1000067767
Erschienen in Archives of Data Science, Series A
Band 1
Heft 1
Seiten 157-176
Lizenz CC BY-SA 3.0 DE: Creative Commons Namensnennung – Weitergabe unter gleichen Bedingungen 3.0 Deutschland
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