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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial
|
|---|---|
| Published in |
Frontiers in immunology, January 2018
|
| DOI | 10.3389/fimmu.2017.01807 |
| Pubmed ID | |
| Authors |
Alex Rubinsteyn, Julia Kodysh, Isaac Hodes, Sebastien Mondet, Bulent Arman Aksoy, John P. Finnigan, Nina Bhardwaj, Jeffrey Hammerbacher |
| Abstract |
This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline that identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The personalized genomic vaccine (PGV) pipeline is modular and consists of independently usable tools and software libraries. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations. |
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 % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 79 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 23 | 29% |
| Student > Ph. D. Student | 14 | 18% |
| Other | 9 | 11% |
| Student > Master | 6 | 8% |
| Student > Bachelor | 3 | 4% |
| Other | 9 | 11% |
| Unknown | 15 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 17 | 22% |
| Immunology and Microbiology | 13 | 16% |
| Agricultural and Biological Sciences | 11 | 14% |
| Computer Science | 6 | 8% |
| Medicine and Dentistry | 6 | 8% |
| Other | 9 | 11% |
| Unknown | 17 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 30. 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 14 April 2021.
All research outputs
#1,288,681
of 25,382,440 outputs
Outputs from Frontiers in immunology
#1,120
of 31,537 outputs
Outputs of similar age
#30,101
of 451,277 outputs
Outputs of similar age from Frontiers in immunology
#30
of 641 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 31,537 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has done particularly well, scoring higher than 96% 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 451,277 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 641 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.