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 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.
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Segal, E., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 34, 166–176 (2003)
Basso, K., Magolin, A., Califano, A.: Reverse engineering of regulatory networks in human B cells. Nature Genetics 37, 382–390 (2005)
Morrissey, E.R., Juárez, M.A., Denby, K.J., Burroughs, N.J.: On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics 26(18), 2305–2312 (2010)
Carro, M.S., Califano, A., Iavarone, A.: The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–325 (2010)
Wang, K., Saito, M., Califano, A.: Genome-wide identification of post-translational modulators of transcription factor activity in human B-cells. Nature Biotechnology 27(9), 829–839 (2009)
Tomancak, P., Beaton, A., Lewis, S.E., Richards, S., Celniker, S.E., Rubin, G.M.: Systematic determination of patterns of gene expression during drosophila embryogenesis. Genome Biology 3(2), 1–14 (2002)
Khan, L., Wang, L.: Automatic ontology derivation using clustering for image classification. In: 8th International Workshop on Multimedia Info Sys. (2002)
Jing, Y., Baluja, S.: Pagerank for product image search. In: WWW, pp. 307–316 (2008)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian networks to analyze expression data. J. Comput. Biol. 7(3-4), 601–620 (2000)
Pournara, I., Wernisch, L.: Reconstruction of gene networks using Bayesian learning and manipulation experiments. Bioinformatics 20(17), 2934–2942 (2004)
Ma, S., Gong, Q., Bohnert, H.J.: An arabidopsis gene network based on the graphical gaussian model. Genome Res. 17, 1614–1625 (2007)
Dobra, A., West, M.: Sparse graphical models for exploring gene expression data. J. Multivariate Analysis 90(1), 196–212 (2004)
Gardner, T., di Bernardo, D., Lorenz, D., Collins, J.J.: Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629), 102–105 (2003)
Bansal, M., Belcastro, V., Ambesi-Impiombato, A., di Bernardo, D.: How to infer gene networks from expression profiles. Molecular Systems Biology 3(78) (2007)
Hache, H., Lehrach, H., Herwig, R.: Reverse engineering of gene regulatory networks: A comparative study. EURASIP Journal on Bioinformatics and Systems Biology 2009 (2009)
Bach, F., Jordan, M.: Learning graphical models with mercer kernels. In: Becker, S., Thrun, S., Obermayer, K. (eds.) NIPS, vol. 15 (2002)
Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. In: ICML, pp. 341–349. Morgan Kaufmann (1998)
Gartner, T., Flach, P.A., Kowalczyk, A., Smola, A.J.: Multi-instance kernels. In: ICML, pp. 179–186 (2002)
Ravikumar, P., Wainwright, M., Raskutti, G., Yu, B.: High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence. Electronic Journal of Statistics (2011)
Banerjee, O., Ghaoui, L.E., d’Aspremont, A., Natsoulis, G.: Convex optimization techniques for fitting sparse gaussian graphical models. In: ICML, pp. 89–96 (2006)
Friedman, J., Hastie, T., Tibshirani, R.: Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432–441 (2007)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B 58(1), 267–288 (1996)
Puniyani, K., Faloutsos, C., Xing, E.: SPEX2: automated concise extraction of spatial gene expression patterns from fly embryo ISH images. Bioinformatics 26(12), 47–56 (2010)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Seventh International Conference on Computer Vision, Kerkyra, Greece, pp. 1150–1157 (1999)
Persson, P., Strang, G.: A simple mesh generator in matlab. SIAM, 329–345 (2004)
Frise, E., Hammonds, A., Celniker, S.: Systematic image-driven analysis of the spatial drosophila embryonic expression landscape. Mol. Sys. Biology 6(345) (2010)
Peterson, J.S., Barkett, M., McCall, K.: Stage-specific regulation of caspase activity in drosophila oogenesis. Dev. Biology 260(1), 113–123 (2003)
Giot, L., Bader, J.S., Brouwer, C., et al.: A protein interaction map of Drosophila melanogaster. Science 302(5651), 1727–1736 (2003)
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Neural Information Processing Systems, pp. 849–856. MIT Press (2001)
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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. https://doi.org/10.1007/978-3-642-33783-3_6
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DOI: https://doi.org/10.1007/978-3-642-33783-3_6
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