Abstract
BackgroundBRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. DRENDA (Disease RElated ENzyme information DAtabase) complements BRENDA with a focus on the automatic search and categorization of enzyme and disease related information from title and abstracts of primary publications. In a two-step procedure DRENDA makes use of text mining and machine learning methods.ResultsCurrently enzyme and disease related references are biannually updated as part of the standard BRENDA update. 910,897 relations of EC-numbers and diseases were extracted from titles or abstracts and are included in the second release in 2010. The enzyme and disease entity recognition has been successfully enhanced by a further relation classification via machine learning. The classification step has been evaluated by a 5-fold cross validation and achieves an F1 score between 0.802 ± 0.032 and 0.738 ± 0.033 depending on the categories and pre-processing procedures. In the eventual DRENDA content every category reaches a classification specificity of at least 96.7% and a precision that ranges from 86-98% in the highest confidence level, and 64-83% for the smallest confidence level associated with higher recall.ConclusionsThe DRENDA processing chain analyses PubMed, locates references with disease-related information on enzymes and categorises their focus according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The categorisation gives an impression on the focus of the located references. Thus, the relation categorisation can facilitate orientation within the rapidly growing number of references with impact on diseases and enzymes. The DRENDA information is available as additional information in BRENDA.
Highlights
BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information
Enzyme and Disease related references The content of DRENDA focuses on information on enzymes and ignores other proteins which may be of biomedical relevance in a physiological and pathophysiological context. This selection is caused by the available extended dictionaries of enzyme names and on the close relationship of DRENDA as a text mining derived knowledge source which contributes to the expert annotated information of BRENDA [7]
DRENDA provides broad information on enzymes and diseases arranged according to Enzyme class (EC) number and disease name
Summary
BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. On the one hand the work of manual expert information extraction is time-consuming and expensive and on the other hand computational power gets cheaper and the growth of publications in the biomedical field is tremendous. Applications in this area quickly gain more importance. The data are extracted from literature abstracts using text-mining procedures
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