Genomic single rule learning with an ontology-based refinement operator

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322364" target="_blank" >RIV/68407700:21230/18:00322364 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.enbik.cz/enbik2018/abs/u166_p35_s1_P.doc" target="_blank" >http://www.enbik.cz/enbik2018/abs/u166_p35_s1_P.doc</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Genomic single rule learning with an ontology-based refinement operator

Popis výsledku v původním jazyce

Rule learning is a kind of machine learning method that induces a set of classification rules from a given set of training examples. As a well-known representative of this learners, we can adduce CN2, RIPPER, or PRIM. All of them use if-then statement for corresponding hypothesis formulation where the antecedent is in the form of a conjunction of logical terms, and the consequent is a class label. From a bioinformatician point of view, these learners are suitable especially for their easy and clear interpretation of hypothesis on the contrary of a neural network, for example. The other thing that can help biologists interpret their data in a more natural way is a background knowledge. Nowadays, the most popular form of background knowledge in the field of bioinformatics are ontologies, especially Gene Ontology or Disease Ontology. There are other types of structured databases such as KEGG, that can also be interpreted as an ontology or a taxonomy. In our work, we combine these two concepts, rule learning and ontologies/taxonomies, together

Název v anglickém jazyce

Genomic single rule learning with an ontology-based refinement operator

Popis výsledku anglicky

Rule learning is a kind of machine learning method that induces a set of classification rules from a given set of training examples. As a well-known representative of this learners, we can adduce CN2, RIPPER, or PRIM. All of them use if-then statement for corresponding hypothesis formulation where the antecedent is in the form of a conjunction of logical terms, and the consequent is a class label. From a bioinformatician point of view, these learners are suitable especially for their easy and clear interpretation of hypothesis on the contrary of a neural network, for example. The other thing that can help biologists interpret their data in a more natural way is a background knowledge. Nowadays, the most popular form of background knowledge in the field of bioinformatics are ontologies, especially Gene Ontology or Disease Ontology. There are other types of structured databases such as KEGG, that can also be interpreted as an ontology or a taxonomy. In our work, we combine these two concepts, rule learning and ontologies/taxonomies, together

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/NV17-31398A" target="_blank" >NV17-31398A: Dlouhé nekódující RNA u myelodysplastického syndromu: klinický význam a implikace pro patogenezi</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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