Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Genre
Journal ArticleDate
2012-05-23Author
Yuan, LWoodard, A
Ji, S
Jiang, Y
Zhou, ZH
Kumar, S
Ye, J
Subject
AnimalsData Mining
Databases, Factual
Drosophila melanogaster
Gene Expression Profiling
Internet
Molecular Sequence Annotation
Pattern Recognition, Automated
Software
Support Vector Machine
Permanent link to this record
http://hdl.handle.net/20.500.12613/5461
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Show full item recordDOI
10.1186/1471-2105-13-107Abstract
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. © 2012 Yuan et al.; licensee BioMed Central Ltd.Citation to related work
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http://dx.doi.org/10.34944/dspace/5443