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Novel semantic similarity measure improves an integrative approach to predicting gene functional associations

Overview of attention for article published in BMC Systems Biology, March 2013
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1 tweeter

Citations

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Readers on

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36 Mendeley
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1 CiteULike
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Title
Novel semantic similarity measure improves an integrative approach to predicting gene functional associations
Published in
BMC Systems Biology, March 2013
DOI 10.1186/1752-0509-7-22
Pubmed ID
Abstract

Elucidation of the direct/indirect protein interactions and gene associations is required to fully understand the workings of the cell. This can be achieved through the use of both low- and high-throughput biological experiments and in silico methods. We present GAP (Gene functional Association Predictor), an integrative method for predicting and characterizing gene functional associations. GAP integrates different biological features using a novel taxonomy-based semantic similarity measure in predicting and prioritizing high-quality putative gene associations. The proposed similarity measure increases information gain from the available gene annotations. The annotation information is incorporated from several public pathway databases, Gene Ontology annotations as well as drug and disease associations from the scientific literature.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Portugal 1 3%
France 1 3%
United States 1 3%
Canada 1 3%
Unknown 31 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 22%
Student > Ph. D. Student 8 22%
Professor 6 17%
Student > Master 6 17%
Other 5 14%
Other 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 47%
Biochemistry, Genetics and Molecular Biology 9 25%
Computer Science 5 14%
Medicine and Dentistry 2 6%
Psychology 1 3%
Other 2 6%

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 01 May 2013.
All research outputs
#3,075,582
of 4,507,280 outputs
Outputs from BMC Systems Biology
#425
of 670 outputs
Outputs of similar age
#60,554
of 89,522 outputs
Outputs of similar age from BMC Systems Biology
#16
of 25 outputs
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