Abstract:BioC is a simple XML format for text, annotations and relations, and was developed to achieve interoperability for biomedical text processing. Following the success of BioC in BioCreative IV, the BioCreative V BioC track addressed a collaborative task to build an assistant system for BioGRID curation. In this paper, we describe the framework of the collaborative BioC task and discuss our findings based on the user survey. This track consisted of eight subtasks including gene/protein/organism named entity recog… Show more
“…Useful text passages could contain mentions of the PPI or GI, or they could contain evidence in the form of important keywords that describe the experimental methods or the interaction types, which were employed by the authors. Figure 1.Annotation interface for the BioC-BioGRID corpus (5). Overlapping annotation types are shown in yellow in the interface.…”
Section: Methodsmentioning
confidence: 99%
“…Annotation interface for the BioC-BioGRID corpus (5). Overlapping annotation types are shown in yellow in the interface.…”
Section: Methodsmentioning
confidence: 99%
“…The BioCreative V Workshop was held in September 2015 and consisted of five tasks: the Collaborative Biocurator Assistant Task (BioC task) (5), the CHEMDNER-patents task (6), the CDR task (7), the BEL task (8, 9) and the User Interactive Task (10). Our focus is the BioC task.…”
A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL:
http://bioc.sourceforge.net/BioC-BioGRID.html
“…Useful text passages could contain mentions of the PPI or GI, or they could contain evidence in the form of important keywords that describe the experimental methods or the interaction types, which were employed by the authors. Figure 1.Annotation interface for the BioC-BioGRID corpus (5). Overlapping annotation types are shown in yellow in the interface.…”
Section: Methodsmentioning
confidence: 99%
“…Annotation interface for the BioC-BioGRID corpus (5). Overlapping annotation types are shown in yellow in the interface.…”
Section: Methodsmentioning
confidence: 99%
“…The BioCreative V Workshop was held in September 2015 and consisted of five tasks: the Collaborative Biocurator Assistant Task (BioC task) (5), the CHEMDNER-patents task (6), the CDR task (7), the BEL task (8, 9) and the User Interactive Task (10). Our focus is the BioC task.…”
A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL:
http://bioc.sourceforge.net/BioC-BioGRID.html
“…For example, in the most recent BioCreative competition in 2015 one particular task focused on the development of a curation interface designed specifically for BioGRID curators (55), based on the BioC standard, an extensible mark-up language created to allow interoperability between biomedical text processing systems (56). This BioCreative task also produced an innovative text-based dataset that tracked all statements used by curators to infer interaction data and phenotypes (20).…”
Section: Text Miningmentioning
confidence: 99%
“…The BioGRID curation pipeline will be enhanced through the integration of ever more sophisticated text-mining tools, which will be implemented in collaboration with text-mining groups and the BioCreative consortium (19,48,49,55,79,80). These efforts will be augmented by collaborations with diverse database partners, including MODs, phenotype databases, and chemical databases.…”
The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
Protein–protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web‐based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state‐of‐the‐art tools. Moreover, it outperforms state‐of‐the‐art tools in the accuracy of PPI relation extraction measured by F‐score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn.
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