ArticleDatabase : the journal of biological databases and curation2016
Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.Briefings in bioinformatics · 2022Review
- Causal Biological Network Model for Inflammasome Signaling Applied for Interpreting Transcriptomic Changes in Various Inflammatory States.International journal of inflammation · 2022Article
- Extraction of causal relations based on SBEL and BERT model.Database : the journal of biological databases and curation · 2021Article
- BpForms and BcForms: a toolkit for concretely describing non-canonical polymers and complexes to facilitate global biochemical networks.Genome biology · 2020Article
- Hierarchical sequence labeling for extracting BEL statements from biomedical literature.BMC medical informatics and decision making · 2019Article
- Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.Briefings in bioinformatics · 2019Review
- Combining relation extraction with function detection for BEL statement extraction.Database : the journal of biological databases and curation · 2019Article
- Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine.Database : the journal of biological databases and curation · 2019Article
- The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.Database : the journal of biological databases and curation · 2019Article
- BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences.Database : the journal of biological databases and curation · 2017Article
- Collaborative relation annotation and quality analysis in Markyt environment.Database : the journal of biological databases and curation · 2017Article
- The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.Database : the journal of biological databases and curation · 2017Article
- BelSmile: a biomedical semantic role labeling approach for extracting biological expression language from text.Database : the journal of biological databases and curation · 2016Article
- The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.Database : the journal of biological databases and curation · 2016Article
- BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.Database : the journal of biological databases and curation · 2016Article
- Coreference resolution improves extraction of Biological Expression Language statements from texts.Database : the journal of biological databases and curation · 2016Article
- BELTracker: evidence sentence retrieval for BEL statements.Database : the journal of biological databases and curation · 2016Article
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11 authors.
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Abstract
Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types 'increases' and 'decreases'. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task.Database URL: http://wiki.openbel.org/display/BIOC/Datasets.
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