| Title |
ApreciseKUre: an approach of Precision Medicine in a Rare Disease
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2017
|
| DOI | 10.1186/s12911-017-0438-0 |
| Pubmed ID | |
| Authors |
Ottavia Spiga, Vittoria Cicaloni, Andrea Bernini, Andrea Zatkova, Annalisa Santucci |
| Abstract |
Alkaptonuria (AKU; OMIM:203500) is a classic Mendelian genetic disorder described by Garrod already in 1902. It causes urine to turn black upon exposure to air and also leads to ochronosis as well as early osteoarthritis. Our objective is the implementation of a Precision Medicine (PM) approach to AKU. We present here a novel ApreciseKUre database facilitating the collection, integration and analysis of patient data in order to create an AKU-dedicated "PM Ecosystem" in which genetic, biochemical and clinical resources can be shared among registered researchers. In order to exploit the ApreciseKUre database, we developed an analytic method based on Pearson's correlation coefficient and P value that generates as refreshable correlation matrix. A complete statistical analysis is obtained by associating every pair of parameters to examine the dependence between multiple variables at the same time. Employing this analytic approach, we showed that some clinically used biomarkers are not suitable as prognostic biomarkers in AKU for a more reliable patients' clinical monitoring. We believe this database could be a good starting point for the creation of a new clinical management tool in AKU, which will lead to the development of a deeper knowledge network on the disease and will advance its treatment. Moreover, our approach can serve as a personalization model paradigm for other inborn errors of metabolism or rare diseases in general. |
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 % |
|---|---|---|
| Spain | 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 16% |
| Student > Master | 6 | 16% |
| Student > Ph. D. Student | 3 | 8% |
| Student > Doctoral Student | 3 | 8% |
| Student > Bachelor | 3 | 8% |
| Other | 7 | 19% |
| Unknown | 9 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 14% |
| Computer Science | 3 | 8% |
| Medicine and Dentistry | 3 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 8% |
| Agricultural and Biological Sciences | 2 | 5% |
| Other | 10 | 27% |
| Unknown | 11 | 30% |
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 26 April 2017.
All research outputs
#15,453,139
of 22,963,381 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,322
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Outputs of similar age
#193,720
of 308,964 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#22
of 32 outputs
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