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SGFSC: speeding the gene functional similarity calculation based on hash tables

Overview of attention for article published in BMC Bioinformatics, November 2016
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Title
SGFSC: speeding the gene functional similarity calculation based on hash tables
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1294-0
Pubmed ID
Authors

Zhen Tian, Chunyu Wang, Maozu Guo, Xiaoyan Liu, Zhixia Teng

Abstract

In recent years, many measures of gene functional similarity have been proposed and widely used in all kinds of essential research. These methods are mainly divided into two categories: pairwise approaches and group-wise approaches. However, a common problem with these methods is their time consumption, especially when measuring the gene functional similarities of a large number of gene pairs. The problem of computational efficiency for pairwise approaches is even more prominent because they are dependent on the combination of semantic similarity. Therefore, the efficient measurement of gene functional similarity remains a challenging problem. To speed current gene functional similarity calculation methods, a novel two-step computing strategy is proposed: (1) establish a hash table for each method to store essential information obtained from the Gene Ontology (GO) graph and (2) measure gene functional similarity based on the corresponding hash table. There is no need to traverse the GO graph repeatedly for each method with the help of the hash table. The analysis of time complexity shows that the computational efficiency of these methods is significantly improved. We also implement a novel Speeding Gene Functional Similarity Calculation tool, namely SGFSC, which is bundled with seven typical measures using our proposed strategy. Further experiments show the great advantage of SGFSC in measuring gene functional similarity on the whole genomic scale. The proposed strategy is successful in speeding current gene functional similarity calculation methods. SGFSC is an efficient tool that is freely available at http://nclab.hit.edu.cn/SGFSC . The source code of SGFSC can be downloaded from http://pan.baidu.com/s/1dFFmvpZ .

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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 42%
Student > Master 3 25%
Student > Ph. D. Student 2 17%
Student > Bachelor 1 8%
Unknown 1 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 33%
Agricultural and Biological Sciences 3 25%
Computer Science 2 17%
Nursing and Health Professions 1 8%
Immunology and Microbiology 1 8%
Other 0 0%
Unknown 1 8%

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 27 November 2016.
All research outputs
#10,014,391
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#3,872
of 4,195 outputs
Outputs of similar age
#258,150
of 320,156 outputs
Outputs of similar age from BMC Bioinformatics
#103
of 122 outputs
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