title: Text-mining-assisted biocuration workflows in Argo.
abstract: Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk.
authors.affiliation: National Centre for Text Mining, School of Computer Science, University of Manchester, UK and Department of Computer Science, University of the Philippines Diliman, Philippines rafal.rak@manchester.ac.uk.
authors.forename: Rafal
authors.initials: R
authors.lastname: Rak
authors.affiliation: National Centre for Text Mining, School of Computer Science, University of Manchester, UK and Department of Computer Science, University of the Philippines Diliman, PhilippinesNational Centre for Text Mining, School of Computer Science, University of Manchester, UK and Department of Computer Science, University of the Philippines Diliman, Philippines.
authors.forename: Riza Theresa
authors.initials: RT
authors.lastname: Batista-Navarro
authors.affiliation: National Centre for Text Mining, School of Computer Science, University of Manchester, UK and Department of Computer Science, University of the Philippines Diliman, Philippines.
authors.forename: Andrew
authors.initials: A
authors.lastname: Rowley
authors.affiliation: National Centre for Text Mining, School of Computer Science, University of Manchester, UK and Department of Computer Science, University of the Philippines Diliman, Philippines.
authors.forename: Jacob
authors.initials: J
authors.lastname: Carter
authors.affiliation: National Centre for Text Mining, School of Computer Science, University of Manchester, UK and Department of Computer Science, University of the Philippines Diliman, Philippines.
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D066289:Data Curation / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D020407:Internet; D058977:Molecular Sequence Annotation; D012984:Software; D013997:Time Factors; D014584:User-Computer Interface