Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the ex...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Xin Zhao, Leo Wang-Kit Cheung
• Keywords: BMCBI 2007, Gene Expression Data, Linear Model, Microarray Gene Expression