You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach
|
|---|---|
| Published in |
BMC Bioinformatics, January 2014
|
| DOI | 10.1186/1471-2105-15-26 |
| Pubmed ID | |
| Authors |
Anirban Mukhopadhyay, Sumanta Ray, Ujjwal Maulik |
| Abstract |
Discovering novel interactions between HIV-1 and human proteins would greatly contribute to different areas of HIV research. Identification of such interactions leads to a greater insight into drug target prediction. Some recent studies have been conducted for computational prediction of new interactions based on the experimentally validated information stored in a HIV-1-human protein-protein interaction database. However, these techniques do not predict any regulatory mechanism between HIV-1 and human proteins by considering interaction types and direction of regulation of interactions. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Iran, Islamic Republic of | 1 | 3% |
| Unknown | 31 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 22% |
| Student > Ph. D. Student | 7 | 22% |
| Student > Master | 5 | 16% |
| Lecturer | 2 | 6% |
| Other | 2 | 6% |
| Other | 3 | 9% |
| Unknown | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 38% |
| Biochemistry, Genetics and Molecular Biology | 4 | 13% |
| Mathematics | 3 | 9% |
| Engineering | 2 | 6% |
| Agricultural and Biological Sciences | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 8 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 January 2014.
All research outputs
#14,772,245
of 22,741,406 outputs
Outputs from BMC Bioinformatics
#5,039
of 7,267 outputs
Outputs of similar age
#181,947
of 306,091 outputs
Outputs of similar age from BMC Bioinformatics
#58
of 93 outputs
Altmetric has tracked 22,741,406 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% 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 306,091 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.