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PLW: Probabilistic Local Walks for detecting protein complexes from protein interaction networks

Overview of attention for article published in BMC Genomics, October 2013
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Title
PLW: Probabilistic Local Walks for detecting protein complexes from protein interaction networks
Published in
BMC Genomics, October 2013
DOI 10.1186/1471-2164-14-s5-s15
Pubmed ID
Authors

Daniel Lin-Kit Wong, Xiao-Li Li, Min Wu, Jie Zheng, See-Kiong Ng

Abstract

Many biological processes are carried out by proteins interacting with each other in the form of protein complexes. However, large-scale detection of protein complexes has remained constrained by experimental limitations. As such, computational detection of protein complexes by applying clustering algorithms on the abundantly available protein-protein interaction (PPI) networks is an important alternative. However, many current algorithms have overlooked the importance of selecting seeds for expansion into clusters without excluding important proteins and including many noisy ones, while ensuring a high degree of functional homogeneity amongst the proteins detected for the complexes.

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Mendeley readers

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 %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Student > Master 2 17%
Professor 2 17%
Unspecified 1 8%
Student > Bachelor 1 8%
Other 1 8%
Unknown 2 17%
Readers by discipline Count As %
Computer Science 6 50%
Unspecified 1 8%
Engineering 1 8%
Unknown 4 33%
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 31 October 2014.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from BMC Genomics
#9,840
of 11,244 outputs
Outputs of similar age
#198,121
of 223,617 outputs
Outputs of similar age from BMC Genomics
#172
of 211 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% 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 223,617 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 211 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.