Casebasedreasoningdrivengeneannotation.pptVIP

优秀精品课件文档资料 Case-based reasoning driven gene annotation G. Christian Overton and Juergen Haas Center for Bioinformatics, Univ. of Pennsylvania 1. Introduction Human Genome Project Flood of information on genetic and physical maps of chromosomes. Automatic data management Under going research “Data mining”, “knowledge discovery” Biological data Extremely abundant Relatively sparse Not generally suitable for statistical method 1. Introduction (cont’d) Case-based reasoning (CBR) Closely model the approach often taken by biologists. Can be viewed as a formalization of one form of reasoning about biological systems. From a DB perspective Provides a convenient framework for exploring issues on maintaining automatically an accurate and consistent view of data. From biology perspective, CBR is a appropriate framework For making heuristic predictions over sparse features. For building a composite view of our current state knowledge. 2. Case-based reasoning CBR in general meaning (Shank [2] in 1980’s) Broad steps (1) Build a case-base of known, characterized and indexed case instances (2) Retrieve a (set of) case(s) from the case-base which is similar to the query case (3) Adapt information from the known case(s) and apply to the query case. (4) Evaluate the solution proposed in the adaptation step and if necessary repeat the adaptation step by, for example, selecting additional cases or repairing the solution proposed in the previous round of adaptation. (5) Store the new solution in the case-base case-base query case (new situation) source cases (well-characterized instances) 3. CBR in biology Similarity by descent (brain development common to all vertebrates) Techniques developed by AI community well suited for reasoning about biological systems Advantages over generalization-based methods data too sparse to drive statistical analysis no sound biological model more efficient and reliable Examples CBR (closely related techniques of memory-based reasoning) applied to predi