Deep convolutional neural networks for annotating gene expression patterns in the mouse brain

BMC Bioinformatics

Methodology Article

Jieping Ye¹  Ravi Mukkamala²  Tao Zeng²  Shuiwang Ji²  Rongjian Li² 

[1] Department of Computational Medicine and Bioinformatics, University of Michigan, 48109, Ann Arbor, MI, USA;Department of Electrical Engineering and Computer Science, University of Michigan, 48109, Ann Arbor, MI, USA;Department of Computer Science, Old Dominion University, 23529, Norfolk, VA, USA;

关键词: Convolutional Neural Network;    Ontology Level;    Annotation Task;    Class Imbalance Problem;    Deep Convolutional Neural Network;   

DOI  :  10.1186/s12859-015-0553-9

 received in 2014-12-02, accepted in 2015-03-27,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundProfiling 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.ResultsWe 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 then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach.ConclusionsDeep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.

【 授权许可】

Unknown   
© Zeng et al.; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.