Title |
Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine
|
---|---|
Published in |
JACC: Basic to Translational Science, June 2017
|
DOI | 10.1016/j.jacbts.2016.11.010 |
Pubmed ID | |
Authors |
Kipp W. Johnson, Khader Shameer, Benjamin S. Glicksberg, Ben Readhead, Partho P. Sengupta, Johan L.M. Björkegren, Jason C. Kovacic, Joel T. Dudley |
Abstract |
The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 16 | 46% |
Canada | 2 | 6% |
India | 2 | 6% |
Switzerland | 2 | 6% |
Italy | 1 | 3% |
Bosnia and Herzegovina | 1 | 3% |
United Kingdom | 1 | 3% |
China | 1 | 3% |
Israel | 1 | 3% |
Other | 2 | 6% |
Unknown | 6 | 17% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 20 | 57% |
Scientists | 13 | 37% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 156 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 24 | 15% |
Student > Ph. D. Student | 21 | 13% |
Student > Master | 14 | 9% |
Other | 13 | 8% |
Student > Bachelor | 11 | 7% |
Other | 32 | 21% |
Unknown | 41 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 28 | 18% |
Computer Science | 19 | 12% |
Biochemistry, Genetics and Molecular Biology | 13 | 8% |
Engineering | 11 | 7% |
Agricultural and Biological Sciences | 9 | 6% |
Other | 26 | 17% |
Unknown | 50 | 32% |