| Title |
The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations
|
|---|---|
| Published in |
Journal of the American Medical Informatics Association, October 2016
|
| DOI | 10.1093/jamia/ocw148 |
| Pubmed ID | |
| Authors |
Linda Huang, Helen Fernandes, Hamid Zia, Peyman Tavassoli, Hanna Rennert, David Pisapia, Marcin Imielinski, Andrea Sboner, Mark A Rubin, Michael Kluk, Olivier Elemento |
| Abstract |
This paper describes the Precision Medicine Knowledge Base (PMKB; https://pmkb.weill.cornell.edu), an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations. PMKB was built using the Ruby on Rails Web application framework. Leveraging existing standards such as the Human Genome Variation Society variant description format, we implemented a data model that links variants to tumor-specific and tissue-specific interpretations. Key features of PMKB include support for all major variant types, standardized authentication, distinct user roles including high-level approvers, and detailed activity history. A REpresentational State Transfer (REST) application-programming interface (API) was implemented to query the PMKB programmatically. At the time of writing, PMKB contains 457 variant descriptions with 281 clinical-grade interpretations. The EGFR, BRAF, KRAS, and KIT genes are associated with the largest numbers of interpretable variants. PMKB's interpretations have been used in over 1500 AmpliSeq tests and 750 whole-exome sequencing tests. The interpretations are accessed either directly via the Web interface or programmatically via the existing API. An accurate and up-to-date knowledge base of genomic alterations of clinical significance is critical to the success of precision medicine programs. The open-access, programmatically accessible PMKB represents an important attempt at creating such a resource in the field of oncology. The PMKB was designed to help collect and maintain clinical-grade mutation interpretations and facilitate reporting for clinical cancer genomic testing. The PMKB was also designed to enable the creation of clinical cancer genomics automated reporting pipelines via an API. |
X Demographics
The data shown below were collected from the profiles of 42 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 | 20 | 48% |
| Argentina | 1 | 2% |
| Spain | 1 | 2% |
| India | 1 | 2% |
| Belgium | 1 | 2% |
| Montenegro | 1 | 2% |
| Philippines | 1 | 2% |
| Australia | 1 | 2% |
| United Kingdom | 1 | 2% |
| Other | 5 | 12% |
| Unknown | 9 | 21% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 20 | 48% |
| Scientists | 16 | 38% |
| Practitioners (doctors, other healthcare professionals) | 4 | 10% |
| Science communicators (journalists, bloggers, editors) | 2 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 105 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 25% |
| Student > Ph. D. Student | 18 | 17% |
| Student > Master | 11 | 10% |
| Student > Bachelor | 10 | 10% |
| Other | 7 | 7% |
| Other | 12 | 11% |
| Unknown | 21 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 21% |
| Agricultural and Biological Sciences | 15 | 14% |
| Medicine and Dentistry | 13 | 12% |
| Computer Science | 12 | 11% |
| Engineering | 4 | 4% |
| Other | 10 | 10% |
| Unknown | 29 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 28. 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 18 July 2023.
All research outputs
#1,371,749
of 25,225,182 outputs
Outputs from Journal of the American Medical Informatics Association
#345
of 3,273 outputs
Outputs of similar age
#24,724
of 321,503 outputs
Outputs of similar age from Journal of the American Medical Informatics Association
#7
of 21 outputs
Altmetric has tracked 25,225,182 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 3,273 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one has done well, scoring higher than 89% of its peers.
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We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.