Semantic multi-classifier systems for the analysis of gene expression profiles
Lausser, Ludwig; Schmid, Florian; Platzer, Matthias; Sillanpää, Mikko J.; Kestler, Hans A. (2016-04-20)
Lausser, Ludwig, Schmid, Florian, Platzer, Matthias, Sillanpää, Mikko J. and Kestler, Hans A. (2016) Semantic multi-classifier systems for the analysis of gene expression profiles. Archives of Data Science, Series A 1(1), p. 157-176.DOI:10.5445/KSP/1000058747/09
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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 sucha 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.
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