| Title |
Predicting Network Activity from High Throughput Metabolomics
|
|---|---|
| Published in |
PLoS Computational Biology, July 2013
|
| DOI | 10.1371/journal.pcbi.1003123 |
| Pubmed ID | |
| Authors |
Shuzhao Li, Youngja Park, Sai Duraisingham, Frederick H. Strobel, Nooruddin Khan, Quinlyn A. Soltow, Dean P. Jones, Bali Pulendran |
| Abstract |
The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 27% |
| India | 2 | 18% |
| Japan | 2 | 18% |
| Canada | 1 | 9% |
| Peru | 1 | 9% |
| Unknown | 2 | 18% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 6 | 55% |
| Members of the public | 4 | 36% |
| Science communicators (journalists, bloggers, editors) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 626 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | <1% |
| United Kingdom | 4 | <1% |
| Germany | 2 | <1% |
| Switzerland | 1 | <1% |
| Netherlands | 1 | <1% |
| South Africa | 1 | <1% |
| Sweden | 1 | <1% |
| Brazil | 1 | <1% |
| Argentina | 1 | <1% |
| Other | 3 | <1% |
| Unknown | 606 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 162 | 26% |
| Researcher | 117 | 19% |
| Student > Master | 64 | 10% |
| Student > Bachelor | 45 | 7% |
| Student > Doctoral Student | 41 | 7% |
| Other | 93 | 15% |
| Unknown | 104 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 127 | 20% |
| Agricultural and Biological Sciences | 122 | 19% |
| Chemistry | 48 | 8% |
| Medicine and Dentistry | 33 | 5% |
| Immunology and Microbiology | 25 | 4% |
| Other | 127 | 20% |
| Unknown | 144 | 23% |