TY - JOUR
T1 - Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
AU - Yuan, Lei
AU - Woodard, Alexander
AU - Ji, Shuiwang
AU - Jiang, Yuan
AU - Zhou, Zhi Hua
AU - Kumar, Sudhir
AU - Ye, Jieping
N1 - Funding Information:
We thank Bernard Van Emden and Michael McCutchan for help with access to the gene expression data. This work is supported in part by the National Institutes of Health grants (LM010730, HG002516), the National Science Foundation grants (IIS-0953662, DBI-1147134), and the National Science Foundation of China grants (60975043, 2010CB327903).
PY - 2012/5/23
Y1 - 2012/5/23
N2 - Background: 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.Results: 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.Conclusions: 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.
AB - Background: 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.Results: 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.Conclusions: 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.
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U2 - 10.1186/1471-2105-13-107
DO - 10.1186/1471-2105-13-107
M3 - Article
C2 - 22621237
AN - SCOPUS:84861332716
SN - 1471-2105
VL - 13
JO - BMC bioinformatics
JF - BMC bioinformatics
IS - 1
M1 - 107
ER -