Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models
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.
Cite
Text
Puniyani and Xing. "Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33783-3_6Markdown
[Puniyani and Xing. "Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/puniyani2012eccv-inferring/) doi:10.1007/978-3-642-33783-3_6BibTeX
@inproceedings{puniyani2012eccv-inferring,
title = {{Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models}},
author = {Puniyani, Kriti and Xing, Eric P.},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {72-85},
doi = {10.1007/978-3-642-33783-3_6},
url = {https://mlanthology.org/eccv/2012/puniyani2012eccv-inferring/}
}