| Title |
Virtual Pharmacist: A Platform for Pharmacogenomics
|
|---|---|
| Published in |
PLOS ONE, October 2015
|
| DOI | 10.1371/journal.pone.0141105 |
| Pubmed ID | |
| Authors |
Ronghai Cheng, Ross Ka-Kit Leung, Yao Chen, Yidan Pan, Yin Tong, Zhoufang Li, Luwen Ning, Xuefeng B. Ling, Jiankui He |
| Abstract |
We present Virtual Pharmacist, a web-based platform that takes common types of high-throughput data, namely microarray SNP genotyping data, FASTQ and Variant Call Format (VCF) files as inputs, and reports potential drug responses in terms of efficacy, dosage and toxicity at one glance. Batch submission facilitates multivariate analysis or data mining of targeted groups. Individual analysis consists of a report that is readily comprehensible to patients and practioners who have basic knowledge in pharmacology, a table that summarizes variants and potential affected drug response according to the US Food and Drug Administration pharmacogenomic biomarker labeled drug list and PharmGKB, and visualization of a gene-drug-target network. Group analysis provides the distribution of the variants and potential affected drug response of a target group, a sample-gene variant count table, and a sample-drug count table. Our analysis of genomes from the 1000 Genome Project underlines the potentially differential drug responses among different human populations. Even within the same population, the findings from Watson's genome highlight the importance of personalized medicine. Virtual Pharmacist can be accessed freely at http://www.sustc-genome.org.cn/vp or installed as a local web server. The codes and documentation are available at the GitHub repository (https://github.com/VirtualPharmacist/vp). Administrators can download the source codes to customize access settings for further development. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 18% |
| Mexico | 1 | 9% |
| United States | 1 | 9% |
| New Zealand | 1 | 9% |
| Canada | 1 | 9% |
| Unknown | 5 | 45% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 82% |
| Scientists | 2 | 18% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 68 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 19% |
| Student > Master | 12 | 17% |
| Student > Ph. D. Student | 10 | 14% |
| Student > Bachelor | 8 | 12% |
| Other | 6 | 9% |
| Other | 6 | 9% |
| Unknown | 14 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 16% |
| Computer Science | 11 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 13% |
| Biochemistry, Genetics and Molecular Biology | 8 | 12% |
| Medicine and Dentistry | 5 | 7% |
| Other | 7 | 10% |
| Unknown | 18 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 15 November 2015.
All research outputs
#3,225,190
of 23,577,761 outputs
Outputs from PLOS ONE
#42,535
of 202,084 outputs
Outputs of similar age
#46,366
of 285,276 outputs
Outputs of similar age from PLOS ONE
#983
of 5,665 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 202,084 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one has done well, scoring higher than 78% 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 285,276 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 5,665 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.