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Scientific Data Mining: Emerging Developments and Challenges

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

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Scientific Data Mining: Emerging Developments and Challenges

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  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

  5. 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

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

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

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

  9. 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

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