| Title |
Bedeutung von Big Data für die molekulare Diagnostik
|
|---|---|
| Published in |
Zeitschrift für Rheumatologie, March 2018
|
| DOI | 10.1007/s00393-018-0436-3 |
| Pubmed ID | |
| Authors |
M. Bonin-Andresen, B. Smiljanovic, B. Stuhlmüller, T. Sörensen, A. Grützkau, T. Häupl |
| Abstract |
Big data analysis raises the expectation that computerized algorithms may extract new knowledge from otherwise unmanageable vast data sets. What are the algorithms behind the big data discussion? In principle, high throughput technologies in molecular research already introduced big data and the development and application of analysis tools into the field of rheumatology some 15 years ago. This includes especially omics technologies, such as genomics, transcriptomics and cytomics. Some basic methods of data analysis are provided along with the technology, however, functional analysis and interpretation requires adaptation of existing or development of new software tools. For these steps, structuring and evaluating according to the biological context is extremely important and not only a mathematical problem. This aspect has to be considered much more for molecular big data than for those analyzed in health economy or epidemiology. Molecular data are structured in a first order determined by the applied technology and present quantitative characteristics that follow the principles of their biological nature. These biological dependencies have to be integrated into software solutions, which may require networks of molecular big data of the same or even different technologies in order to achieve cross-technology confirmation. More and more extensive recording of molecular processes also in individual patients are generating personal big data and require new strategies for management in order to develop data-driven individualized interpretation concepts. With this perspective in mind, translation of information derived from molecular big data will also require new specifications for education and professional competence. |
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 % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 17% |
| Lecturer | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Other | 1 | 8% |
| Student > Ph. D. Student | 1 | 8% |
| Other | 3 | 25% |
| Unknown | 3 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 25% |
| Medicine and Dentistry | 3 | 25% |
| Economics, Econometrics and Finance | 1 | 8% |
| Computer Science | 1 | 8% |
| Unknown | 4 | 33% |
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 10 March 2018.
All research outputs
#18,590,133
of 23,026,672 outputs
Outputs from Zeitschrift für Rheumatologie
#309
of 452 outputs
Outputs of similar age
#258,490
of 332,626 outputs
Outputs of similar age from Zeitschrift für Rheumatologie
#2
of 9 outputs
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