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A new method of finding groups of coexpressed genes and conditions of coexpression

Overview of attention for article published in BMC Bioinformatics, November 2016
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2 tweeters
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1 Facebook page

Citations

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2 Dimensions

Readers on

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40 Mendeley
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Title
A new method of finding groups of coexpressed genes and conditions of coexpression
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1356-3
Pubmed ID
Authors

Rajat Anand, Srikanth Ravichandran, Samrat Chatterjee

Abstract

To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters. We present here an algorithm, clustered groups, which finds groups of co-expressed genes and conditions of co-expression with minimal input from user. We used random datasets to derive a cutoff on the basis of which we filtered the resultant groups and showed that this can improve the relevance of obtained groups. We showed that the proposed algorithm performs better than other known algorithms on both real and synthetic datasets. We have also shown its application on a temporal microarray dataset by extracting biclusters and biological information hidden in those biclusters. Clustered groups is an algorithm which finds groups of co-expressed genes and conditions of co-expression using only a single parameter. We have shown that it works better than other existing algorithms. It can be used to find these groups in different data types such as microarray, proteomics, metabolomics etc.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 30%
Researcher 8 20%
Professor 4 10%
Student > Ph. D. Student 4 10%
Student > Bachelor 3 8%
Other 4 10%
Unknown 5 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 30%
Agricultural and Biological Sciences 9 23%
Medicine and Dentistry 5 13%
Engineering 3 8%
Computer Science 3 8%
Other 1 3%
Unknown 7 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 November 2016.
All research outputs
#8,510,470
of 14,137,367 outputs
Outputs from BMC Bioinformatics
#3,425
of 5,341 outputs
Outputs of similar age
#193,937
of 380,324 outputs
Outputs of similar age from BMC Bioinformatics
#274
of 425 outputs
Altmetric has tracked 14,137,367 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,341 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 380,324 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 425 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.