Finding semantic patterns in omics data using concept rule learning with an ontology-based refinement operator

0539595 - ÚMG 2021 RIV GB eng J - Článek v odborném periodiku
Malinka, František - Zelezny, F. - Kléma, J.
Finding semantic patterns in omics data using concept rule learning with an ontology-based refinement operator.
BioData Mining. Roč. 13, č. 1 (2020), č. článku 13. ISSN 1756-0381. E-ISSN 1756-0381
Institucionální podpora: RVO:68378050
Klíčová slova: Symbolic machine learning * Enrichment analysis * Ontology * Taxonomy * Gene expression * Biclustering
Obor OECD: Biochemistry and molecular biology
Impakt faktor: 2.522, rok: 2020
Způsob publikování: Open access
https://biodatamining.biomedcentral.com/articles/10.1186/s13040-020-00219-6

Background: Identification of non-trivial and meaningful patterns in omics data is one of the most important biological tasks. The patterns help to better understand biological systems and interpret experimental outcomes. A well-established method serving to explain such biological data is Gene Set Enrichment Analysis. However, this type of analysis is restricted to a specific type of evaluation. ing from details, the analyst provides a sorted list of genes and ontological annotations of the individual genes, the method outputs a subset of ontological terms enriched in the gene list. Here, in contrary to enrichment analysis, we introduce a new tool/framework that allows for the induction of more complex patterns of 2-dimensional binary omics data. This extension allows to discover and describe semantically coherent biclusters.
Trvalý link: http://hdl.handle.net/11104/0317303