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

Overview of attention for article published in BMC Bioinformatics, May 2012
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2 X users

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Title
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Published in
BMC Bioinformatics, May 2012
DOI 10.1186/1471-2105-13-107
Pubmed ID
Authors

Lei Yuan, Alexander Woodard, Shuiwang Ji, Yuan Jiang, Zhi-Hua Zhou, Sudhir Kumar, Jieping Ye

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Colombia 1 3%
Austria 1 3%
Unknown 30 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 27%
Student > Ph. D. Student 5 15%
Professor 3 9%
Professor > Associate Professor 3 9%
Student > Master 3 9%
Other 5 15%
Unknown 5 15%
Readers by discipline Count As %
Computer Science 12 36%
Engineering 5 15%
Agricultural and Biological Sciences 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Mathematics 1 3%
Other 5 15%
Unknown 6 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 May 2013.
All research outputs
#15,866,607
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#5,477
of 7,400 outputs
Outputs of similar age
#106,101
of 165,820 outputs
Outputs of similar age from BMC Bioinformatics
#69
of 100 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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