A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database

A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database Matthew Shardlow1, Nhung Nguyen1, Gareth Owen2, Steve Turner2, Claire O’Donovan2, Andrew Leach2, John McNaught1, Sophia Ananiadou1 National Centre for Text Mining, University of Manchester1, European Bioinformatics Institute2 {matthew.shardlow, nhung.nguyen, john.mcnaught, sophia.ananiadou}@manchester.ac.uk, {gowen, turner, odonovan, arl}@ebi.ac.uk Abstract We present a new corpus of 200 abstracts and 100 full text papers which have been annotated with named entities and relations in the biomedical domain as part of the OpenMinTeD project. This corpus facilitates the goal in OpenMinTeD of making text and data mining accessible to the users who need it most. We describe the process we took to annotate the corpus with entities (Metabolite, Chemical, Protein, Species, Biological Activity and Spectral Data) and relations (Isolated From, Associated With, Binds With and Metabolite Of). We report inter-annotator agreement (using F-score) for entities of between 0.796 and 0.892 using a strict matching protocol and between 0.875 and 0.963 using a relaxed matching protocol. For relations we report inter annotator agreement of between 0.591 and 0.693 using a strict matching protocol and between 0.744 and 0.793 using a relaxed matching protocol. We describe how this corpus can be used within ChEBI to facilitate text and data mining and how the integration of this work with the OpenMinTeD text and data mining platform will aid curation of ChEBI and other biomedical databases. Keywords: Text Mining, Corpus, Bioinformatics 1. Introduction and Background The ChEBI (Chemical Entities of Biological Interest) database (Degtyarenko et al., 2008; Hastings et al., 2016) is a freely available, electronic dictionary and ontology of small molecules. ChEBI was created to help researchers in the field of molecular biology who need to know the structure, names, and properties of the small molecules that they encounter in their research. There are a number of freely-available chemical databases. Most of them are cre- ated by an automatic ‘pipeline’ process and contain infor- mation on polymers, industrial chemicals, synthetic inter- mediates, etc. Their sheer size creates problems for users, as any search may result in hundreds or even thousands of answers. For non-expert users it is very difficult to deter- mine which, if any, is the compound they are really looking for. By contrast, the focus of the ChEBI database is on high quality rather than quantity. ChEBI is manually curated and focuses on the requirements of the molecular biology community. Manual curation as- sures the high quality of the database, but it also makes it an expensive database to produce, particularly so for our focus of metabolites. Users who would like to add a new metabo- lite, may only know a research code or a trivial name from which it is not possible to deduce a structure. The cura- tor then has to search through scientific literature to find as much information as possible about the new metabolite. The information is likely to include: • In which species is it present? • Does it have any interesting biological properties, ap- plications, …

Content truncated to 500 words