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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Chapter 4: Protein Interactions and Disease
|
|---|---|
| Published in |
PLoS Computational Biology, December 2012
|
| DOI | 10.1371/journal.pcbi.1002819 |
| Pubmed ID | |
| Authors |
Mileidy W. Gonzalez, Maricel G. Kann |
| Abstract |
Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 27% |
| France | 2 | 18% |
| Colombia | 1 | 9% |
| Netherlands | 1 | 9% |
| Canada | 1 | 9% |
| Japan | 1 | 9% |
| Unknown | 2 | 18% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 64% |
| Members of the public | 3 | 27% |
| Science communicators (journalists, bloggers, editors) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 535 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 6 | 1% |
| United States | 5 | <1% |
| Spain | 4 | <1% |
| Brazil | 4 | <1% |
| Italy | 3 | <1% |
| Germany | 2 | <1% |
| France | 2 | <1% |
| Japan | 2 | <1% |
| Canada | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 504 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 117 | 22% |
| Researcher | 80 | 15% |
| Student > Master | 69 | 13% |
| Student > Bachelor | 52 | 10% |
| Student > Postgraduate | 25 | 5% |
| Other | 76 | 14% |
| Unknown | 116 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 128 | 24% |
| Biochemistry, Genetics and Molecular Biology | 110 | 21% |
| Chemistry | 45 | 8% |
| Computer Science | 38 | 7% |
| Medicine and Dentistry | 29 | 5% |
| Other | 58 | 11% |
| Unknown | 127 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 24 March 2022.
All research outputs
#2,378,346
of 25,845,895 outputs
Outputs from PLoS Computational Biology
#2,119
of 9,052 outputs
Outputs of similar age
#22,001
of 291,205 outputs
Outputs of similar age from PLoS Computational Biology
#27
of 120 outputs
Altmetric has tracked 25,845,895 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,052 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has done well, scoring higher than 76% of its peers.
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 291,205 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.