Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
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 net...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3434040/ https://www.ncbi.nlm.nih.gov/pubmed/22621237 http://dx.doi.org/10.1186/1471-2105-13-107 |
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author | Yuan, Lei Woodard, Alexander Ji, Shuiwang Jiang, Yuan Zhou, Zhi-Hua Kumar, Sudhir Ye, Jieping |
author_facet | Yuan, Lei Woodard, Alexander Ji, Shuiwang Jiang, Yuan Zhou, Zhi-Hua Kumar, Sudhir Ye, Jieping |
author_sort | Yuan, Lei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3434040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34340402012-09-10 Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval Yuan, Lei Woodard, Alexander Ji, Shuiwang Jiang, Yuan Zhou, Zhi-Hua Kumar, Sudhir Ye, Jieping BMC Bioinformatics Research Article 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. BioMed Central 2012-05-23 /pmc/articles/PMC3434040/ /pubmed/22621237 http://dx.doi.org/10.1186/1471-2105-13-107 Text en Copyright ©2012 Yuan et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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. |
spellingShingle | Research Article Yuan, Lei Woodard, Alexander Ji, Shuiwang Jiang, Yuan Zhou, Zhi-Hua Kumar, Sudhir Ye, Jieping Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval |
title | Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval |
title_full | Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval |
title_fullStr | Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval |
title_full_unstemmed | Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval |
title_short | Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval |
title_sort | learning sparse representations for fruit-fly gene expression pattern image annotation and retrieval |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3434040/ https://www.ncbi.nlm.nih.gov/pubmed/22621237 http://dx.doi.org/10.1186/1471-2105-13-107 |
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