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Open Access Open Badges Research article

Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval

Lei Yuan12, Alexander Woodard12, Shuiwang Ji3, Yuan Jiang4, Zhi-Hua Zhou4, Sudhir Kumar15 and Jieping Ye12*

Author Affiliations

1 Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, AZ 85287, USA

2 Ira A. Fulton Schools of Engineering, Arizona State University, AZ 85287, USA

3 Department of Computer Science, Old Dominion University, VA 23529, USA

4 National Key Laboratory for Novel Software Technology, Nanjing University, China

5 School of Life Sciences, Arizona State University, AZ 85287, USA

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BMC Bioinformatics 2012, 13:107  doi:10.1186/1471-2105-13-107

Published: 23 May 2012



Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.


In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.


We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.