Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)


New bio-technologies are being developed that allow high-throughput imaging of gene expressions, where each image captures the spatial gene expression pattern of a single gene in the tissue of interest. This paper addresses the problem of automatically inferring a gene interaction network from such images. We propose a novel kernel-based graphical model learning algorithm, that is both convex and consistent. The algorithm uses multi-instance kernels to compute similarity between the expression patterns of different genes, and then minimizes the L 1 regularized Bregman divergence to estimate a sparse gene interaction network. We apply our algorithm on a large, publicly available data set of gene expression images of Drosophila embryos, where our algorithm makes novel and interesting predictions.


Annotation Term Gaussian Graphical Model Gene Interaction Network Bregman Divergence Mercer Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computer ScienceCarnegie Mellon UniversityUSA

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