| Title |
PROPER: global protein interaction network alignment through percolation matching
|
|---|---|
| Published in |
BMC Bioinformatics, December 2016
|
| DOI | 10.1186/s12859-016-1395-9 |
| Pubmed ID | |
| Authors |
Ehsan Kazemi, Hamed Hassani, Matthias Grossglauser, Hassan Pezeshgi Modarres |
| Abstract |
The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch . |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 38% |
| Professor | 2 | 13% |
| Student > Doctoral Student | 2 | 13% |
| Student > Master | 2 | 13% |
| Researcher | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 3 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 38% |
| Biochemistry, Genetics and Molecular Biology | 3 | 19% |
| Agricultural and Biological Sciences | 1 | 6% |
| Engineering | 1 | 6% |
| Unknown | 5 | 31% |
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 21 December 2016.
All research outputs
#12,787,113
of 22,912,409 outputs
Outputs from BMC Bioinformatics
#3,639
of 7,305 outputs
Outputs of similar age
#195,289
of 418,945 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 133 outputs
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We're also able to compare this research output to 133 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 57% of its contemporaries.