Differential network analysis from cross-platform gene expression data

Differential network analysis from cross-platform gene expression data

Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different group...

Cross-Platform Analysis with Binarized Gene Expression Data

With widespread use of microarray technology as a potential diagnostics tool, the comparison of results obtained from the use of different platforms is of interest. When inference methods are designed using data collected using a particular platform, they are unlikely to work directly on measurements taken from a different type of array. We report on this cross-platform transfer problem, and sh...

Identifying differential networks based on multi-platform gene expression data.

Exploring how the structure of a gene regulatory network differs between two different disease states is fundamental for understanding the biological mechanisms behind disease development and progression. Recently, with rapid advances in microarray technologies, gene expression profiles of the same patients can be collected from multiple microarray platforms. However, previous differential netw...

Cross-Platform Social Network Analysis

Bayesian Differential Analysis of Gene Expression Data

This paper describes a novel Bayesian method for the differential analysis of large scale gene expression data. The novelty of the method is the use of a contamination model that integrates the different sources of variability that affect gene expression data measured with microarray technology, thus removing the need for arbitrary normalization.