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1	ODIN: an advanced interface for the curation of biomedical literature
	Rinaldi, Fabio; Clematide, S; Schneider, G; Romacker, M; Vachon, Th
	In: Rinaldi, Fabio; Clematide, S; Schneider, G; Romacker, M; Vachon, Th (2010). ODIN: an advanced interface for the curation of biomedical literature. In: Biocuration 2010, Tokyo, Japan, 11 October 2010 - 14 October 2010. (2010)
	Abstract: We present ODIN (Ontogene Document INspector): a system for interactive curation of biomedical literature, developed within the scope of the SASEBio project (Semi-Automated Semantic Enrichment of the Biomedical Literature), as a collaboration between the OntoGene group at the University of Zurich and the NITAS/TMS group of Novartis Pharma AG. The purpose of the system is to allow a human annotator/curator to leverage upon the results of an advanced text mining system in order to enhance the speed and effectiveness of the annotation process. The OntoGene system takes as input a document (e.g a full paper from PubMed Central) and processes it with a custom NLP pipeline, which includes Named Entity recognition and relation extraction. Entities which are currently supported include proteins, genes, experimental methods, cell lines, species. Entities detected in the input document are disambiguated with respect to a reference database (UniProt, EntrezGene, NCBI taxonomy, PSI-MI ontology). The annotated documents are handed back to the ODIN interface, which allows multiple display modalities. The curator/annotator can view the whole document with in-line annotations highlighted, or can browse the extracted entities and be pointed back to the mentions of the entities within the original document. All entity mentions are entirely editable: the curator can easily add or delete any of them, and also change their extent (i.e. add/remove words to its right or left) with a simple click of the mouse. Different entity views are supported, with sorting capabilities according to different criteria (entity type, entity mention, confidence score, etc.). Selective highlighting of text units (e.g. sentences containing desired entities) is supported. Additionally, extensive logging functionalities are provided. All documents and entities are fully interlinked to reference databases, for the purpose of simplified inspection. Entities can be grouped in classes (e.g. by species) and actions can be applied to whole classes, for selective editing or removal.
	Keyword: 000 Computer science; 410 Linguistics; Institute of Computational Linguistics; knowledge & systems
	URL: https://doi.org/10.1038/npre.2010.5169.1 https://www.zora.uzh.ch/id/eprint/46738/4/Rinaldi_Clematide_Schneider_2010V.pdf https://www.zora.uzh.ch/id/eprint/46738/ https://doi.org/10.5167/uzh-46738
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