Bioinformatics meets data mining: time to dance?

Participants on one side of the room were given several lessons in the principles of data mining, which are themselves still evolving within the field of computer science. The opening address by Heikki Mannila (University of Helsinki, Helsinki, Finland) pointed out that data mining is much more than the application of standard database queries. Certainly, the underlying data model is critical to the mining exercise, in terms of both access efficiency and associated data, but there is first the issue of local analysis vs global analysis. Local analysis might only be one particular protein family within a large corpus, assuming that the data associated with that family can be identified. The methodology, for example, clustering, might not be applied as it would be to a global study of the complete corpus. Data mining in both instances is a process that analyses data to generate descriptive and/or predictive models that can be used to understand patterns or relationships in the data. Associated with this modeling must be a good statistical analysis and, of course, visualization of the findings; all these were covered at the conference.Algorithms for pattern matching include decision trees, Naive-Bayes association, K-nearest neighbor and neural networks, which are already familiar to many bioinformaticians, but generally in the context of motif discovery, tandem repeat finding, and so on, associated with DNA sequences and not diffuse clusters of array expression or other data. Recognized problems in data mining included lack of use, thus far, of Bayesian approaches, the often temporal nature of the data being studied, how to interpret outliers and the ability to use background knowledge to strengthen the predictive outcome. However, it should be noted that Ron Taylor (National Cancer Institute, National Institutes of Health, Bethesda, MD, USA) described a Bayesian similarity measure applied to different experiments in a large array database.Given the importance of the underlying database to data mining, several posters and presentations described new integrative approaches for accessing data from different biological sources, with emphasis on linking gene identification to available functional information. Rolf Apweiler (EMBL–EBI, Hinxton, UK) described InterPro, an integrated documentation resource for protein families, domains and functional sites. Importantly, there was a follow-on conference for those interested in standards for the storage of microarray data. As someone involved in applying improved and consistent annotation to the 27 years of legacy macromolecular structure data as found in the Protein Data Bank, it would be useful to get the storage of array data right, from the beginning. Already, there might be a divide between what the European Biotechnology Institute is spearheading and the recent indications from the US National Center of Biotechnology Information.Finally, there were talks and posters on approaches to the visualization of massive datasets. David Gilbert (City University, London, UK) presented particularly exciting work, beyond the use of unrooted trees, for the display of pairwise comparison data; he and his colleagues use two new three-dimensional (3D) clustering algorithms for the visualization of a 3D space.The conference (http://industry.ebi.ac.uk/datamining99) highlighted for me that at the moment we are stumbling, rather than dancing, but I have no doubt that there will be more dances, and new biology will be forthcoming as we learn the steps. As the Kinks say, ‘Come dancing’.