| Title |
Construction and analysis of protein–protein interaction networks
|
|---|---|
| Published in |
Automated Experimentation, February 2010
|
| DOI | 10.1186/1759-4499-2-2 |
| Pubmed ID | |
| Authors |
Karthik Raman |
| Abstract |
Protein-protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein-protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations. |
Mendeley readers
The data shown below were compiled from readership statistics for 274 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 5 | 2% |
| United States | 4 | 1% |
| India | 3 | 1% |
| Germany | 2 | <1% |
| Sweden | 1 | <1% |
| Iran, Islamic Republic of | 1 | <1% |
| Brazil | 1 | <1% |
| Spain | 1 | <1% |
| Russia | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 255 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 65 | 24% |
| Researcher | 44 | 16% |
| Student > Master | 44 | 16% |
| Student > Bachelor | 23 | 8% |
| Student > Doctoral Student | 18 | 7% |
| Other | 42 | 15% |
| Unknown | 38 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 100 | 36% |
| Biochemistry, Genetics and Molecular Biology | 55 | 20% |
| Computer Science | 30 | 11% |
| Engineering | 9 | 3% |
| Mathematics | 7 | 3% |
| Other | 18 | 7% |
| Unknown | 55 | 20% |
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 04 September 2019.
All research outputs
#8,533,995
of 25,371,288 outputs
Outputs from Automated Experimentation
#5
of 6 outputs
Outputs of similar age
#55,690
of 184,762 outputs
Outputs of similar age from Automated Experimentation
#2
of 2 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one scored the same or higher as 1 of them.
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 184,762 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.