Improving classification in protein structure databases using text mining
1 Bioinformatics Group, Department of Computer Science, University College of London, London, WC1E 6BT, UK
2 Department of Structural and Molecular Biology, University College of London, London, WC1E 6BT, UK
BMC Bioinformatics 2009, 10:129 doi:10.1186/1471-2105-10-129Published: 5 May 2009
The classification of protein domains in the CATH resource is primarily based on structural comparisons, sequence similarity and manual analysis. One of the main bottlenecks in the processing of new entries is the evaluation of 'borderline' cases by human curators with reference to the literature, and better tools for helping both expert and non-expert users quickly identify relevant functional information from text are urgently needed. A text based method for protein classification is presented, which complements the existing sequence and structure-based approaches, especially in cases exhibiting low similarity to existing members and requiring manual intervention. The method is based on the assumption that textual similarity between sets of documents relating to proteins reflects biological function similarities and can be exploited to make classification decisions.
An optimal strategy for the text comparisons was identified by using an established gold standard enzyme dataset. Filtering of the abstracts using a machine learning approach to discriminate sentences containing functional, structural and classification information that are relevant to the protein classification task improved performance. Testing this classification scheme on a dataset of 'borderline' protein domains that lack significant sequence or structure similarity to classified proteins showed that although, as expected, the structural similarity classifiers perform better on average, there is a significant benefit in incorporating text similarity in logistic regression models, indicating significant orthogonality in this additional information. Coverage was significantly increased especially at low error rates, which is important for routine classification tasks: 15.3% for the combined structure and text classifier compared to 10% for the structural classifier alone, at 10-3 error rate. Finally when only the highest scoring predictions were used to infer classification, an extra 4.2% of correct decisions were made by the combined classifier.
We have described a simple text based method to classify protein domains that demonstrates an improvement over existing methods. The method is unique in incorporating structural and text based classifiers directly and is particularly useful in cases where inconclusive evidence from sequence or structure similarity requires laborious manual classification.