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
Semantic multi-classifier systems for the analysis of gene expression profiles
|Author:||Lausser, Ludwig1,2; Schmid, Florian2; Platzer, Matthias1;|
1Leibniz Institute on Aging, Jena, Germany
2Medical Systems Biology, Ulm University, Germany
3University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018053125065
KIT Scientific Publishing,
|Publish Date:|| 2018-05-31
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.
Archives of data science. Series A
|Pages:||157 - 176|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
112 Statistics and probability
113 Computer and information sciences
The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n 602783 (to HAK), the German Research Foundation (DFG, SFB 1074 project Z1 to HAK), and the Federal Ministry of Education and Research (BMBF, Gerontosys II, Forschungskern SyStaR, project ID 0315894A to HAK).
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