EECS Publication
A Combinatorial Approach to the Analysis of Differential Gene Expression Data: The Use of Graph Algorithms for Disease Prediction and Screening
Michael A. Langston, Lan Lin, Xinxia Peng, Nicole E. Baldwin, Christopher T. Symons, Bing Zhang and Jay R. Snoddy.
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 edgeweighted graphs. Two somewhat orthogonal algorithms 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 such as these on patient populations with known disease profiles, so that they can then be employed to classify and predict the likelihood of disease in patient populations whose profiles are not known in advance. These novel strategies are in marked contrast with Bayesian and other well-known techniques. Encouraging results are reported.
Published 2004-01-01 05:00:00 as ut-cs-04-514 (ID:175)