DOI 10.1186/s12859-015-0553-9 METHODOLOGY ARTICLE Open Access Deep

convolutional neural networks for annotating gene expression patterns in the mouse brain Tao Zeng1, Rongjian Li1, Ravi Mukkamala1, Jieping Ye2,3 and Shuiwang Ji1* Background: Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. Results: We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were the