A simple and exact Laplacian clustering of complex networking phenomena: application to gene expression profiles.

ABSTRACT: Unraveling of the unified networking characteristics of complex networking phenomena is of great interest yet a formidable task. There is currently no simple strategy with a rigorous framework. Using an analogy to the exact algebraic property for a transition matrix of a master equation in statistical physics, we propose a method based on a Laplacian matrix for the discovery and prediction of new classes in the unsupervised complex networking phenomena where the class of each sample is completely unknown. Using this proposed Laplacian approach, we can simultaneously discover different classes and determine the identity of each class. Through an illustrative test of the Laplacian approach applied to real datasets of gene expression profiles, leukemia data [Golub TR, et al. (1999) Science 28

SUBMITTER: Kim C 

PROVIDER: S-EPMC2393820 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature