DataMininginBioinformatics.ppt

* Cross Validation: Example One-tier cross validation Train on different data than test data Two-tier cross validation The score from one-tier cross validation is used by the bias optimizer to select the best learning algorithm parameters (# of control points) . The more you optimize the more you over-fit. The second tier is to measure the level of over-fit (unbiased measure of accuracy). Useful for comparing learning algorithms with control parameters that are optimized. Number of folds is not optimized. Computational complexity: #folds of top tier X #folds of bottom tier X #control points X CPU of algorithm * Summary Microarray problem Computational biology Major objective of microarray technology Input and output of data analysis Data mining and image analysis steps Image normalization, grid alignment, feature construction Data mining techniques Prior knowledge Expected results of data mining Validation Issues Cross validation techniques Overviewheader Peter Bajcsy, PhD Automated Learning Group National Center for Supercomputing Applications University of Illinois pbajcsy@ncsa.uiuc.edu January 31, 2002 优秀精品课件文档资料 Data Mining in Bioinformatics * Outline Introduction Overview of Microarray Problem Image Analysis Data Mining Validation Summary * Introduction: Recommended Literature 1. Bioinformatics – The Machine Learning Approach by P. Baldi S. Brunak, 2nd edition, The MIT Press, 2001 2. Data Mining – Concepts and Techniques by J. Han M. Kamber, Morgan Kaufmann Publishers, 2001 3. Pattern Classification by R. Duda, P. Hart and D. Stork, 2nd edition, John Wiley Sons, 2001 * Introduction: Microarray Problem in Bioinformatics Domain Problems in Bioinformatics Domain Data production at the levels of molecules, cells, organs, organisms, populations Integration of structure and function data, gene expression data, pathway data, phenotypic and clinical data, … Prediction of Molecular Function and Structure Computational biology: synthesis (simulations) a