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Mendeley readers
Attention Score in Context
| Title |
A computational method for drug repositioning using publicly available gene expression data
|
|---|---|
| Published in |
BMC Bioinformatics, December 2015
|
| DOI | 10.1186/1471-2105-16-s17-s5 |
| Pubmed ID | |
| Authors |
KM Shabana, KA Abdul Nazeer, Meeta Pradhan, Mathew Palakal |
| Abstract |
The identification of new therapeutic uses of existing drugs, or drug repositioning, offers the possibility of faster drug development, reduced risk, lesser cost and shorter paths to approval. The advent of high throughput microarray technology has enabled comprehensive monitoring of transcriptional response associated with various disease states and drug treatments. This data can be used to characterize disease and drug effects and thereby give a measure of the association between a given drug and a disease. Several computational methods have been proposed in the literature that make use of publicly available transcriptional data to reposition drugs against diseases. In this work, we carry out a data mining process using publicly available gene expression data sets associated with a few diseases and drugs, to identify the existing drugs that can be used to treat genes causing lung cancer and breast cancer. Three strong candidates for repurposing have been identified- Letrozole and GDC-0941 against lung cancer, and Ribavirin against breast cancer. Letrozole and GDC-0941 are drugs currently used in breast cancer treatment and Ribavirin is used in the treatment of Hepatitis C. |
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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 41 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 26% |
| Student > Master | 8 | 19% |
| Student > Ph. D. Student | 5 | 12% |
| Other | 5 | 12% |
| Professor | 3 | 7% |
| Other | 6 | 14% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 19% |
| Medicine and Dentistry | 6 | 14% |
| Agricultural and Biological Sciences | 6 | 14% |
| Biochemistry, Genetics and Molecular Biology | 4 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 7% |
| Other | 11 | 26% |
| Unknown | 4 | 10% |
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 08 December 2015.
All research outputs
#18,431,664
of 22,834,308 outputs
Outputs from BMC Bioinformatics
#6,320
of 7,288 outputs
Outputs of similar age
#280,337
of 388,302 outputs
Outputs of similar age from BMC Bioinformatics
#139
of 159 outputs
Altmetric has tracked 22,834,308 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,288 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 5th percentile – i.e., 5% 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 388,302 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.