| Title |
The development of precision medicine in clinical practice
|
|---|---|
| Published in |
Clinical and Translational Medicine, August 2015
|
| DOI | 10.1186/s40169-015-0069-y |
| Pubmed ID | |
| Authors |
Mingyan He, Jinglin Xia, Mohamed Shehab, Xiangdong Wang |
| Abstract |
Precision medicine allows a dramatic expansion of biological data, while there is still an urgent need to understand and insight the exact meaning of those data to human health and disease. This has led to an increasing wealth of data unanalyzed. The concept of precision medicine is about the customization of healthcare, with decisions and practices tailored to an individual patient based on their intrinsic biology in addition to clinical "signs and symptoms". Construction of a standardized model for the integration of data from various platforms is the central mission of the 'New Disease Management Model'. The model is helpful for the development of new taxonomy of diseases and subtypes, to personalize therapy based on patient genetic profiles. A rapid progression of precision therapy has been made recently. Clinical trials have shown the therapeutic efficacy of discovered and developed therapeutic agents has improved. However, next-generation drugs would be designed for disease subtypes with more specificity, efficacy and lower toxicity. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 3% |
| Germany | 1 | 3% |
| Unknown | 38 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 20% |
| Researcher | 8 | 20% |
| Student > Bachelor | 6 | 15% |
| Student > Ph. D. Student | 5 | 13% |
| Other | 4 | 10% |
| Other | 1 | 3% |
| Unknown | 8 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 10 | 25% |
| Medicine and Dentistry | 5 | 13% |
| Computer Science | 3 | 8% |
| Agricultural and Biological Sciences | 3 | 8% |
| Engineering | 3 | 8% |
| Other | 7 | 18% |
| Unknown | 9 | 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 08 September 2015.
All research outputs
#19,944,091
of 25,373,627 outputs
Outputs from Clinical and Translational Medicine
#712
of 1,060 outputs
Outputs of similar age
#191,677
of 279,406 outputs
Outputs of similar age from Clinical and Translational Medicine
#4
of 4 outputs
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