Belief Combination for Uncertainty Reduction in Microarray Gene Expression Pattern Analysis.

Many classification methods are used in microarray gene expression data analysis to identify genes that are predictive to clinical outcomes (survival/fatal) of certain diseases. However, the reliability of these methods is often not well established due to the imprecision of the method and uncertainty of the dataset. In this paper, a knowledge-based belief reasoning system (BRS) is proposed to solve the problem by dealing with the uncertainties inherent in the results of various classification methods. Through the belief combination process, we pursue a means to reduce the uncertainty and improve the reliability of classification so that the underlying features of gene behavior recorded in the microarray expression profiles could be convincingly revealed. [ABSTRACT FROM AUTHOR]