Using the OntoGene pipeline for the triage task of BioCreative 2012

Rinaldi, Fabio; Clematide, Simon; Hafner, Simon; Schneider, Gerold; Grigonytė, Gintarė; Romacker, Martin; Vachon, Therese

doi:10.1093/database/bas053

Cited by 20 publications

(18 citation statements)

References 15 publications

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“…As such, the OntoGene team has developed several successful biomedical text mining applications, centered on an XML-based pipeline, that have been tested in community-wide competitions, with top-ranked achievements (59, 60). The OntoGene system is used to generate annotations that can be accessed and modified through the OntoGene Document Inspector interface.…”

Section: Current Bioc Workmentioning

confidence: 99%

BioC: a minimalist approach to interoperability for biomedical text processing

et al. 2013

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A vast amount of scientific information is encoded in natural language text, and the quantity of such text has become so great that it is no longer economically feasible to have a human as the first step in the search process. Natural language processing and text mining tools have become essential to facilitate the search for and extraction of information from text. This has led to vigorous research efforts to create useful tools and to create humanly labeled text corpora, which can be used to improve such tools. To encourage combining these efforts into larger, more powerful and more capable systems, a common interchange format to represent, store and exchange the data in a simple manner between different language processing systems and text mining tools is highly desirable. Here we propose a simple extensible mark-up language format to share text documents and annotations. The proposed annotation approach allows a large number of different annotations to be represented including sentences, tokens, parts of speech, named entities such as genes or diseases and relationships between named entities. In addition, we provide simple code to hold this data, read it from and write it back to extensible mark-up language files and perform some sample processing. We also describe completed as well as ongoing work to apply the approach in several directions. Code and data are available at http://bioc.sourceforge.net/.Database URL: http://bioc.sourceforge.net/

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Section: Current Bioc Workmentioning

confidence: 99%

BioC: a minimalist approach to interoperability for biomedical text processing

et al. 2013

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“…The quality of the text mining tools developed by the group is demonstrated by top-ranked results achieved at several community-organized text mining competitions (16, 19, 21). …”

Section: Text Mining Pipelinementioning

confidence: 99%

Strategies towards digital and semi-automated curation in RegulonDB

et al. 2017

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Experimentally generated biological information needs to be organized and structured in order to become meaningful knowledge. However, the rate at which new information is being published makes manual curation increasingly unable to cope. Devising new curation strategies that leverage upon data mining and text analysis is, therefore, a promising avenue to help life science databases to cope with the deluge of novel information. In this article, we describe the integration of text mining technologies in the curation pipeline of the RegulonDB database, and discuss how the process can enhance the productivity of the curators.Specifically, a named entity recognition approach is used to pre-annotate terms referring to a set of domain entities which are potentially relevant for the curation process. The annotated documents are presented to the curator, who, thanks to a custom-designed interface, can select sentences containing specific types of entities, thus restricting the amount of text that needs to be inspected. Additionally, a module capable of computing semantic similarity between sentences across the entire collection of articles to be curated is being integrated in the system. We tested the module using three sets of scientific articles and six domain experts. All these improvements are gradually enabling us to obtain a high throughput curation process with the same quality as manual curation.

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“…As a way to verify the quality of the core text mining functionalities, the underlying text mining pipeline has been used to perform several tasks which have been formally evaluated within the context of community-organized text mining evaluations campaigns ("shared tasks"), such as BioCreative [18]. Some of most interesting results include best results in the detection of protein-protein interactions in BioCreative 2009 [19], top-ranked results in several tasks of BioCreative 2010 [20], best results in the triage task of BioCreative 2012 [21].…”

Section: Discussionmentioning

confidence: 99%

An Approach towards Semi-automated Biomedical Literature Curation and Enrichment for a Major Biological Database

et al. 2015

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Abstract-As part of a large-scale biocuration project, we are developing innovative techniques to process the biomedical literature and extract information relevant to specific biological investigations. Biological experts routinely extract core information from the scientific literature using a manual process known as scientific curation. The aim of our activity is to improve the efficiency of this process by leveraging upon natural language processing technologies in a text mining system. There are two lines of investigation that we pursue: (1) finding information relevant for curation and present it in an adaptive interface, and (2) use sentence-similarity techniques to create interlinks across articles in order to allow a process of knowledge discovery.

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Using the OntoGene pipeline for the triage task of BioCreative 2012

Cited by 20 publications

References 15 publications

BioC: a minimalist approach to interoperability for biomedical text processing

BioC: a minimalist approach to interoperability for biomedical text processing

Strategies towards digital and semi-automated curation in RegulonDB

An Approach towards Semi-automated Biomedical Literature Curation and Enrichment for a Major Biological Database

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