| Title |
MCAM: Multiple Clustering Analysis Methodology for Deriving Hypotheses and Insights from High-Throughput Proteomic Datasets
|
|---|---|
| Published in |
PLoS Computational Biology, July 2011
|
| DOI | 10.1371/journal.pcbi.1002119 |
| Pubmed ID | |
| Authors |
Kristen M. Naegle, Roy E. Welsch, Michael B. Yaffe, Forest M. White, Douglas A. Lauffenburger |
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 % |
|---|---|---|
| Spain | 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 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 7% |
| United Kingdom | 2 | 2% |
| India | 1 | <1% |
| Germany | 1 | <1% |
| France | 1 | <1% |
| Luxembourg | 1 | <1% |
| Unknown | 95 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 35 | 32% |
| Student > Ph. D. Student | 30 | 28% |
| Other | 7 | 6% |
| Professor > Associate Professor | 7 | 6% |
| Professor | 5 | 5% |
| Other | 20 | 18% |
| Unknown | 5 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 55 | 50% |
| Biochemistry, Genetics and Molecular Biology | 16 | 15% |
| Computer Science | 10 | 9% |
| Chemistry | 7 | 6% |
| Engineering | 6 | 6% |
| Other | 8 | 7% |
| Unknown | 7 | 6% |