Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models

  • Kriti Puniyani
  • Eric P. Xing
Conference paper

DOI: 10.1007/978-3-642-33783-3_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)
Cite this paper as:
Puniyani K., Xing E.P. (2012) Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models. In: Fitzgibbon A., Lazebnik S., Perona P., Sato Y., Schmid C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7577. Springer, Berlin, Heidelberg

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kriti Puniyani
    • 1
  • Eric P. Xing
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityUSA

Personalised recommendations