Development of a classification scheme for disease-related enzyme information

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Abstract

Background: 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. 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.Results: Currently 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.Conclusions: The 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. © 2011 Söhngen et al; licensee BioMed Central Ltd.

Figures

  • Figure 1 A schematic illustration of the DRENDA work flow. The BRENDA enzyme names and synonyms and the MeSH disease terms are used as dictionaries. The PubMed abstracts and titles are searched for co-occurring disease and enzyme entities. A test/train corpus was created for training an SVM and classifying the cooccurrence results according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The resulting entries are stored in the DRENDA database.
  • Table 1 A survey of the distribution of the co-occurrence derived results
  • Table 2 Maximal F1 scores achieved for the classification categories and the different preprocessing methods
  • Figure 2 Receiver operating characteristic (ROC) plots of the models, which achieved the maximal F1 scores. The ROC plots shown belong to the models, which achieved the maximal F1 scores (table 2) in the five-fold cross-validation with either a removal (a) or replacement (b) preprocessing applied before the calculation of term weights. The ROC curves are vertical averaged (fixed false positive rates and averages of the corresponding true positive rates of each turn of the five-fold cross validation). In spite of decreasing standard deviation for larger numbers of available training sentences, the largest area under the curve (AUC) is achieved by classifiers for the category therapeutic application, which has least annotated sentences in the test/training corpus. See table 2 for the corresponding scalar AUC values of each plot.
  • Table 3 Results and quality estimate of the category causal interaction
  • Table 4 Results and quality estimate of the category ongoing research
  • Table 5 Results and quality estimate of the category therapeutic application
  • Table 6 Results and quality estimate of the category diagnostic usage

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Söhngen, C., Chang, A., & Schomburg, D. (2011). Development of a classification scheme for disease-related enzyme information. BMC Bioinformatics, 12. https://doi.org/10.1186/1471-2105-12-329

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