Abstract
BackgroundWithin the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations.ResultsThe "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations).ConclusionWe explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications.
Highlights
Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years
Gene Ontology Annotation dataset In order to analyze the associations of proteins with GOterms within scientific articles we used the Gene Ontology Annotation database (GOA) [18]
We developed a database which contained cross linked protein entries derived from several sources, including the HUGO, OMIM, SwissProt, UniGene and LocusLink databases
Summary
Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. There is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations. The extracted information is used to build up annotations within biological databases describing relevant aspects of these proteins These annotations are commonly used to automatically infer annotations to other, sequence-related proteins, as often protein sequence similarity can give clues to functional similarity. As the amount of feasible annotations using manual information extraction is limited, it may not keep up with the pace of article publication and represents a severe bottleneck preventing (page number not for citation purposes)
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