Abstract:
Combinatorial methods are studied in an effort to gauge their potential utility in the analysis of differential gene expression data. Patient and gene relationships are modeled using edge-weighted graphs. Two algorithms with different, but complementary approaches are devised and implemented. One is based on finding optimal cliques within general graphs, the other on isolating near-optimal dominating sets within bipartite graphs. A main goal is to develop methodologies for training algorithms on patient populations with known disease profiles, so that they can be employed to classify and predict the likelihood of disease in patient populations whose profiles are not known. These novel strategies are in marked contrast with Bayesian and other wellknown techniques. Encouraging results are reported.
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Langston, M.A. et al. (2005). A Combinatorial Approach to the Analysis of Differential Gene Expression Data. In: Shoemaker, J.S., Lin, S.M. (eds) Methods of Microarray Data Analysis. Springer, Boston, MA. https://doi.org/10.1007/0-387-23077-7_17
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DOI: https://doi.org/10.1007/0-387-23077-7_17
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