Metamodeling integration architecture for open biomedical ontologies: the GO extensions' case study
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Identifying Gene Ontology Areas for Automated Enrichment
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Ontology refinement for improved information retrieval
Information Processing and Management: an International Journal
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Motivation: Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones. Results: We present a novel method that predicts more detailed concepts by utilizing syntactic relations among the existing concepts. We propose a validation measure for the automatically predicted concepts by matching the concepts to biomedical articles. We also suggest how to find a suitable direction for the extension of a constantly growing ontology such as GO. Availability: http://autogo.biopathway.org Contact: park@nlp.kaist.ac.kr Supplementary information: Supplementary materials are available at Bioinformatics online.