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Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species

by Ludwig Lausser 1,†, Lea Siegle 1,†, Wolfgang Rottbauer 2, Derk Frank 3,4,‡, Steffen Just 2,‡ and Hans A. Kestler 1,*,‡
Institute of Medical Systems Biology, Ulm University, 89069 Ulm, Germany
Department of Internal Medicine II, Ulm University, 89081 Ulm, Germany
Department of Internal Medicine III (Cardiology and Angiology) UKSH, Campus Kiel, 24105 Kiel, Germany
DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, 24105 Kiel, Germany
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
These authors are joint senior authors of this work.
Biomolecules 2018, 8(4), 158;
Received: 20 September 2018 / Revised: 21 November 2018 / Accepted: 21 November 2018 / Published: 26 November 2018
(This article belongs to the Special Issue Biomolecules for Translational Approaches in Cardiology)
Genetic model organisms have the potential of removing blind spots from the underlying gene regulatory networks of human diseases. Allowing analyses under experimental conditions they complement the insights gained from observational data. An inevitable requirement for a successful trans-species transfer is an abstract but precise high-level characterization of experimental findings. In this work, we provide a large-scale analysis of seven weak contractility/heart failure genotypes of the model organism zebrafish which all share a weak contractility phenotype. In supervised classification experiments, we screen for discriminative patterns that distinguish between observable phenotypes (homozygous mutant individuals) as well as wild-type (homozygous wild-types) and carriers (heterozygous individuals). As the method of choice we use semantic multi-classifier systems, a knowledge-based approach which constructs hypotheses from a predefined vocabulary of high-level terms (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Gene Ontology (GO) terms). Evaluating these models leads to a compact description of the underlying processes and guides the screening for new molecular markers of heart failure. Furthermore, we were able to independently corroborate the identified processes in Wistar rats. View Full-Text
Keywords: Heart failure phenotypes; zebrafish; Wistar rat; semantic multi-classifier systems Heart failure phenotypes; zebrafish; Wistar rat; semantic multi-classifier systems
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Lausser, L.; Siegle, L.; Rottbauer, W.; Frank, D.; Just, S.; Kestler, H.A. Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species. Biomolecules 2018, 8, 158.

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