↓ Skip to main content

An integrated clinical and genomic information system for cancer precision medicine

Overview of attention for article published in BMC Medical Genomics, April 2018
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
29 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
An integrated clinical and genomic information system for cancer precision medicine
Published in
BMC Medical Genomics, April 2018
DOI 10.1186/s12920-018-0347-9
Pubmed ID
Authors

Yeongjun Jang, Taekjin Choi, Jongho Kim, Jisub Park, Jihae Seo, Sangok Kim, Yeajee Kwon, Seungjae Lee, Sanghyuk Lee

Abstract

Increasing affordability of next-generation sequencing (NGS) has created an opportunity for realizing genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information presents a huge challenge for clinicians in interpreting the patient's genomic alterations and selecting the optimum approved or investigational therapy. An elaborate and practical information system is urgently needed to support clinical decision as well as to test clinical hypotheses quickly. Here, we present an integrated clinical and genomic information system (CGIS) based on NGS data analyses. Major components include modules for handling clinical data, NGS data processing, variant annotation and prioritization, drug-target-pathway analysis, and population cohort explorer. We built a comprehensive knowledgebase of genes, variants, drugs by collecting annotated information from public and in-house resources. Structured reports for molecular pathology are generated using standardized terminology in order to help clinicians interpret genomic variants and utilize them for targeted cancer therapy. We also implemented many features useful for testing hypotheses to develop prognostic markers from mutation and gene expression data. Our CGIS software is an attempt to provide useful information for both clinicians and scientists who want to explore genomic information for precision oncology.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 17%
Researcher 5 17%
Other 3 10%
Student > Master 3 10%
Student > Postgraduate 2 7%
Other 5 17%
Unknown 6 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 28%
Medicine and Dentistry 4 14%
Agricultural and Biological Sciences 3 10%
Computer Science 2 7%
Immunology and Microbiology 1 3%
Other 3 10%
Unknown 8 28%

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 10 October 2019.
All research outputs
#8,758,368
of 16,004,888 outputs
Outputs from BMC Medical Genomics
#374
of 842 outputs
Outputs of similar age
#129,393
of 280,174 outputs
Outputs of similar age from BMC Medical Genomics
#4
of 8 outputs
Altmetric has tracked 16,004,888 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 842 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% 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 280,174 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 53% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.