| Title |
IntPath--an integrated pathway gene relationship database for model organisms and important pathogens
|
|---|---|
| Published in |
BMC Systems Biology, December 2012
|
| DOI | 10.1186/1752-0509-6-s2-s2 |
| Pubmed ID | |
| Authors |
Hufeng Zhou, Jingjing Jin, Haojun Zhang, Bo Yi, Michal Wozniak, Limsoon Wong |
| Abstract |
Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases. |
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 % |
|---|---|---|
| United States | 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Singapore | 2 | 4% |
| United States | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 47 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 24% |
| Student > Ph. D. Student | 12 | 24% |
| Student > Master | 10 | 20% |
| Student > Bachelor | 6 | 12% |
| Other | 3 | 6% |
| Other | 5 | 10% |
| Unknown | 3 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 29% |
| Computer Science | 14 | 27% |
| Biochemistry, Genetics and Molecular Biology | 8 | 16% |
| Medicine and Dentistry | 5 | 10% |
| Business, Management and Accounting | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 6 | 12% |
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 13 September 2014.
All research outputs
#13,407,734
of 22,753,345 outputs
Outputs from BMC Systems Biology
#476
of 1,142 outputs
Outputs of similar age
#157,413
of 278,953 outputs
Outputs of similar age from BMC Systems Biology
#18
of 56 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 55% 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 278,953 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 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 67% of its contemporaries.