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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378050%3A_____%2F20%3A00539595" target="_blank" >RIV/68378050:_____/20:00539595 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/20:00342306

Result on the web

<a href="https://biodatamining.biomedcentral.com/articles/10.1186/s13040-020-00219-6" target="_blank" >https://biodatamining.biomedcentral.com/articles/10.1186/s13040-020-00219-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13040-020-00219-6" target="_blank" >10.1186/s13040-020-00219-6</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10608 - Biochemistry and molecular biology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BioData Mining

ISSN

1756-0381

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

22

Pages from-to

13

UT code for WoS article

000566165300001

EID of the result in the Scopus database

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