| Title |
Interpretation and Visualization of Non-Linear Data Fusion in Kernel Space: Study on Metabolomic Characterization of Progression of Multiple Sclerosis
|
|---|---|
| Published in |
PLOS ONE, June 2012
|
| DOI | 10.1371/journal.pone.0038163 |
| Pubmed ID | |
| Authors |
Agnieszka Smolinska, Lionel Blanchet, Leon Coulier, Kirsten A. M. Ampt, Theo Luider, Rogier Q. Hintzen, Sybren S. Wijmenga, Lutgarde M. C. Buydens |
| Abstract |
In the last decade data fusion has become widespread in the field of metabolomics. Linear data fusion is performed most commonly. However, many data display non-linear parameter dependences. The linear methods are bound to fail in such situations. We used proton Nuclear Magnetic Resonance and Gas Chromatography-Mass Spectrometry, two well established techniques, to generate metabolic profiles of Cerebrospinal fluid of Multiple Sclerosis (MScl) individuals. These datasets represent non-linearly separable groups. Thus, to extract relevant information and to combine them a special framework for data fusion is required. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 67% |
| France | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 33% |
| Members of the public | 1 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 3% |
| Korea, Republic of | 1 | 1% |
| Italy | 1 | 1% |
| South Africa | 1 | 1% |
| Denmark | 1 | 1% |
| Spain | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 66 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 31% |
| Researcher | 19 | 26% |
| Student > Master | 6 | 8% |
| Other | 3 | 4% |
| Student > Postgraduate | 3 | 4% |
| Other | 9 | 12% |
| Unknown | 11 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 15 | 20% |
| Agricultural and Biological Sciences | 13 | 18% |
| Medicine and Dentistry | 8 | 11% |
| Mathematics | 6 | 8% |
| Biochemistry, Genetics and Molecular Biology | 5 | 7% |
| Other | 14 | 19% |
| Unknown | 13 | 18% |