Enhancing navigation in biomedical databases by community voting and database-driven text classification
1 Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
2 Center for Systems Biology, Massachusetts General Hospital, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
3 Technische Universität München, Institut für Informatik/I12, Garching, Germany
4 Current address: Beckman Coulter Genomics Inc., 500 Cummings Center, Suite 2450, Beverly, MA, USA
5 Current address: German Research Center for Artificial Intelligence (DFKI GmbH), Robert-Hooke-Str. 5, 28359 Bremen, Germany
BMC Bioinformatics 2009, 10:317 doi:10.1186/1471-2105-10-317Published: 3 October 2009
The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them.
Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly.
Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases.