ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Sparse graphical models for exploring gene expression data
Journal of Multivariate Analysis
Convex optimization techniques for fitting sparse Gaussian graphical models
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Reverse engineering of gene regulatory networks: a comparative study
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
Bioinformatics
NP-MuScL: unsupervised global prediction of interaction networks from multiple data sources
RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
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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.