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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ChemDIS: a chemical–disease inference system based on chemical–protein interactions
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2015
|
| DOI | 10.1186/s13321-015-0077-3 |
| Pubmed ID | |
| Authors |
Chun-Wei Tung |
| Abstract |
The characterization of toxicities associated with environmental and industrial chemicals is required for risk assessment. However, we lack the toxicological data for a large portion of chemicals due to the high cost of experiments for a huge number of chemicals. The development of computational methods for identifying potential risks associated with chemicals is desirable for generating testable hypothesis to accelerate the hazard identification process. A chemical-disease inference system named ChemDIS was developed to facilitate hazard identification for chemicals. The chemical-protein interactions from a large database STITCH and protein-disease relationship from disease ontology and disease ontology lite were utilized for chemical-protein-disease inferences. Tools with user-friendly interfaces for enrichment analysis of functions, pathways and diseases were implemented and integrated into ChemDIS. An analysis on maleic acid and sibutramine showed that ChemDIS could be a useful tool for the identification of potential functions, pathways and diseases affected by poorly characterized chemicals. ChemDIS is an integrated chemical-disease inference system for poorly characterized chemicals with potentially affected functions and pathways for experimental validation. ChemDIS server is freely accessible at http://cwtung.kmu.edu.tw/chemdis. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| Taiwan | 1 | 33% |
| United Kingdom | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| Taiwan | 1 | 3% |
| Unknown | 29 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 19% |
| Student > Postgraduate | 4 | 13% |
| Student > Master | 4 | 13% |
| Student > Ph. D. Student | 3 | 10% |
| Student > Doctoral Student | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 16% |
| Computer Science | 5 | 16% |
| Agricultural and Biological Sciences | 4 | 13% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Medicine and Dentistry | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 8 | 26% |
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 20 January 2017.
All research outputs
#14,490,397
of 24,312,464 outputs
Outputs from Journal of Cheminformatics
#695
of 894 outputs
Outputs of similar age
#129,318
of 268,286 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 19 outputs
Altmetric has tracked 24,312,464 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 894 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 20th percentile – i.e., 20% 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 268,286 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.