In this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.
The article presents evaluation of the application of Neo4j graph database to Gene Ontology graph analysis. Graph-based term similarity measures are calculated in order to assess the effectiveness of the system. Two types of common ancestor search are presented and evaluated, and parallel execution of the analysis is also evaluated.
PL
Artykuł przedstawia ocenę zastosowania grafowej bazy danych Neo4j do analizy grafu ontologii Gene Ontology. Ocena systemu została przeprowadzona na podstawie obliczenia bazujących na analizie grafu miar podobieństwa terminów ontologii. Przedstawione i ocenione zostały dwa sposoby wyszukiwania rodziców w grafie. Analizie poddano również równoległą realizację badanych algorytmów.
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