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Identifying conserved protein complexes between species by constructing interolog networks

Overview of attention for article published in BMC Bioinformatics, October 2013
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  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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6 X users

Citations

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48 Mendeley
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Title
Identifying conserved protein complexes between species by constructing interolog networks
Published in
BMC Bioinformatics, October 2013
DOI 10.1186/1471-2105-14-s16-s8
Pubmed ID
Authors

Phi-Vu Nguyen, Sriganesh Srihari, Hon Wai Leong

Abstract

Protein complexes conserved across species indicate processes that are core to cellular machinery (e.g. cell-cycle or DNA damage-repair complexes conserved across human and yeast). While numerous computational methods have been devised to identify complexes from the protein interaction (PPI) networks of individual species, these are severely limited by noise and errors (false positives) in currently available datasets. Our analysis using human and yeast PPI networks revealed that these methods missed several important complexes including those conserved between the two species (e.g. the MLH1-MSH2-PMS2-PCNA mismatch-repair complex). Here, we note that much of the functionalities of yeast complexes have been conserved in human complexes not only through sequence conservation of proteins but also of critical functional domains. Therefore, integrating information of domain conservation might throw further light on conservation patterns between yeast and human complexes.

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The data shown below were collected from the profiles of 6 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 29%
Student > Bachelor 10 21%
Student > Master 8 17%
Student > Doctoral Student 2 4%
Researcher 2 4%
Other 6 13%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 42%
Biochemistry, Genetics and Molecular Biology 12 25%
Computer Science 5 10%
Psychology 1 2%
Medicine and Dentistry 1 2%
Other 0 0%
Unknown 9 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 April 2014.
All research outputs
#13,566,023
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#3,850
of 7,454 outputs
Outputs of similar age
#109,656
of 214,900 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 116 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 45th percentile – i.e., 45% 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 214,900 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.