Parallel Preconditioning with Sparse Approximate Inverses
SIAM Journal on Scientific Computing
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Clustering short time series gene expression data
Bioinformatics
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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The innate immune response is the first line of host defense against infections. This system employs a number of different types of cells which in turn activate different sets of genes. Microarray studies of human and mouse cells infected with various pathogens identified hundreds of differentially expressed genes. However, combining these datasets to identify common and unique response patterns remained a challenge. We developed methods based on probabilistic graphical models to combine expression experiments across species, cells and pathogens. Our method analyzes homologous genes in different species concurrently overcoming problems related to noise and orthology assignments. Using our method we identified both core immune response genes and genes that are activated in macrophages in both human and mouse but not in dendritic cells, and vice versa. Our results shed light on immune response mechanisms and on the differences between various types of cells that are used to fight infecting bacteria. Supporting website: http://www.cs.cmu.edu/~lyongu/pub/immune/