| Title |
A conceptual model for translating omic data into clinical action
|
|---|---|
| Published in |
Journal of Pathology Informatics, August 2015
|
| DOI | 10.4103/2153-3539.163985 |
| Pubmed ID | |
| Authors |
Timothy M. Herr, Suzette J. Bielinski, Erwin Bottinger, Ariel Brautbar, Murray Brilliant, Christopher G. Chute, Joshua Denny, Robert R. Freimuth, Andrea Hartzler, Joseph Kannry, Isaac S. Kohane, Iftikhar J. Kullo, Simon Lin, Jyotishman Pathak, Peggy Peissig, Jill Pulley, James Ralston, Luke Rasmussen, Dan Roden, Gerard Tromp, Marc S. Williams, Justin Starren |
| Abstract |
Genomic, proteomic, epigenomic, and other "omic" data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called "the omic funnel." This model parallels the classic "Data, Information, Knowledge, Wisdom pyramid" and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 29 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 5 | 16% |
| Student > Master | 4 | 13% |
| Researcher | 3 | 10% |
| Other | 3 | 10% |
| Student > Doctoral Student | 3 | 10% |
| Other | 8 | 26% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 19% |
| Engineering | 4 | 13% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Computer Science | 3 | 10% |
| Agricultural and Biological Sciences | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 7 | 23% |
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 07 October 2015.
All research outputs
#20,657,128
of 25,374,917 outputs
Outputs from Journal of Pathology Informatics
#329
of 409 outputs
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#203,625
of 277,321 outputs
Outputs of similar age from Journal of Pathology Informatics
#6
of 7 outputs
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