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Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models
Kriti Puniyani and Eric P. Xing
School of Computer Science, Carnegie Mellon University, USAkpuniyan@cs.cmu.edu
epxing@cs.cmu.edu
Abstract. 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 L1 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.
LNCS 7577, p. 72 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012