PMC

Screen shot of the BRENDA web portal entry with a view on the DRENDA query form.

The quota of intersection of classification categories (numbers × 10³, rounded). The overall amount of distinct EC, disease and PubMed reference combinations in the categories causal interaction (grey), therapeutic application (blue), ongoing research (pink) and diagnostic usage (green) in every DRENDA confidence level 1-4. The number of unassigned combinations is listed in the sets (yellow) at the bottom of each plot.

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 co-occurrence results according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The resulting entries are stored in the DRENDA database.

Access to the DRENDA data. The query form (a) provides several fields for entering search pattern information. The fields can be combined arbitrarily for a refinement of the query and meet individual requirements. As an example, a part of the query result table (b) for "Diabetes mellitus" as requested disease and all entries assigned to the category therapeutic application with a DRENDA confidence level of 3 and 4.

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.