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ChemDIS: a chemical–disease inference system based on chemical–protein interactions

Overview of attention for article published in Journal of Cheminformatics, June 2015
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters

Citations

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14 Dimensions

Readers on

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31 Mendeley
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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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

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 7 23%
Student > Postgraduate 4 13%
Student > Master 4 13%
Student > Ph. D. Student 3 10%
Student > Doctoral Student 2 6%
Other 7 23%
Unknown 4 13%
Readers by discipline Count As %
Computer Science 6 19%
Pharmacology, Toxicology and Pharmaceutical 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 7 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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
#3,154,497
of 11,346,162 outputs
Outputs from Journal of Cheminformatics
#267
of 444 outputs
Outputs of similar age
#71,418
of 228,911 outputs
Outputs of similar age from Journal of Cheminformatics
#11
of 19 outputs
Altmetric has tracked 11,346,162 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 444 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 38th percentile – i.e., 38% 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 228,911 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 65% 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 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.