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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yuan, Lei, Woodard, Alexander, Ji, Shuiwang, Jiang, Yuan, Zhou, Zhi-Hua, Kumar, Sudhir, Ye, Jieping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
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
_version_ 1782242378707369984
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
work_keys_str_mv AT yuanlei learningsparserepresentationsforfruitflygeneexpressionpatternimageannotationandretrieval
AT woodardalexander learningsparserepresentationsforfruitflygeneexpressionpatternimageannotationandretrieval
AT jishuiwang learningsparserepresentationsforfruitflygeneexpressionpatternimageannotationandretrieval
AT jiangyuan learningsparserepresentationsforfruitflygeneexpressionpatternimageannotationandretrieval
AT zhouzhihua learningsparserepresentationsforfruitflygeneexpressionpatternimageannotationandretrieval
AT kumarsudhir learningsparserepresentationsforfruitflygeneexpressionpatternimageannotationandretrieval
AT yejieping learningsparserepresentationsforfruitflygeneexpressionpatternimageannotationandretrieval