1. Scientific Data Mining: Emerging Developments and Challenges F. Seillier-Moiseiwitsch Bioinformatics Research Center Department of Mathematics and Statistics University of Maryland - Baltimore County

2. Bioinformatics: A View from the Trenches

3. Some Needed Developments: Simultaneous data mining of databases Different types of information in separate databases GenBank, PDB, HIV-Web, PubMed, … Data selection Generic solution

4. Some Needed Developments: Simultaneous data mining of databases Same information in different databases Meta-analysis e.g. Gene expression data Pre-processing different technologies sources of variability

6. Some Needed Developments: Data mining of heterogeneous databases Many different types of information in same database e.g. Patient records - diagnostics lab results, DNA, microarray 2D gel images data compression features

8. Some Needed Developments: New Algorithms Molecular evolution Phylogenetic reconstruction Large number of sequences Statistical evolutionary models MCMC, E-M algorithm Parallel processors Emerging models

10. Some Needed Developments: New Algorithms Proteomics images of 2D gels clean up, alignment group composite image biological vs. experimental variability easily updated

11. Some Needed Developments: New Algorithms Functional genomics microarray data background estimation (subjectivity) automation of analytical protocols

12. Some Challenges Public domain software Easily implementation on any computing platform Incorporation of state-of-the-art statistical techniques clustering, classification longitudinal models spatio-temporel models