Gene expression network analysis and applications to immunology

Gene expression network analysis and applications to immunology

UNLABELLED We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and...

Artificial neural network applications in immunology

Artificial neural network (ANN) applications in immunology include simulations of peptide binding to MHC molecules, which present peptides for recognition by the immune system. These peptides are derived from protein antigens and represent prime targets for vaccine discovery. ANN models have proven superior when compared to the alternative models. Applications of ANN models help minimise the nu...

Cluster analysis and its applications to gene expression data.

Gene expression analysis, proteomics, and network discovery.

Acknowledgements We acknowledge support of our research from the European Union (EU Framework Program 6, AGRON-OMICS (LSHG-CT-2006-037704)), the Swiss National Science Foundation, CTI (Swiss Innovation Promotion Agency) and ETH Zurich. We thank the Functional Genomics Center Zurich for support of our profiling experiments. Apologies to all colleagues whose work could not be cited due to space c...

CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis

Novel DNA mlcroarray technologies enable the monitoring of expression levels of thousands of genes simultaneously. This allows a global view on the transcription levels of many (or all) genes when the cell undergoes specific conditions or processes. Analyzing gene expression data requires the clustering of genes into groups with similar expression patterns. We have developed a novel clustering ...