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Attention Score in Context
| Title |
Mining protein interactomes to improve their reliability and support the advancement of network medicine
|
|---|---|
| Published in |
Frontiers in Genetics, September 2015
|
| DOI | 10.3389/fgene.2015.00296 |
| Pubmed ID | |
| Authors |
Gregorio Alanis-Lobato |
| Abstract |
High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 23% |
| Researcher | 5 | 19% |
| Student > Master | 4 | 15% |
| Other | 2 | 8% |
| Student > Bachelor | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 5 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 31% |
| Agricultural and Biological Sciences | 5 | 19% |
| Computer Science | 4 | 15% |
| Immunology and Microbiology | 1 | 4% |
| Physics and Astronomy | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 7 | 27% |
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 07 October 2015.
All research outputs
#15,347,611
of 22,829,083 outputs
Outputs from Frontiers in Genetics
#5,437
of 11,820 outputs
Outputs of similar age
#160,866
of 274,809 outputs
Outputs of similar age from Frontiers in Genetics
#43
of 63 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,820 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.