| Title |
Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach
|
|---|---|
| Published in |
PLOS ONE, April 2010
|
| DOI | 10.1371/journal.pone.0009969 |
| Pubmed ID | |
| Authors |
Beatriz García-Jiménez, David Juan, Iakes Ezkurdia, Eduardo Andrés-León, Alfonso Valencia |
| Abstract |
Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome. |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 3 | 6% |
| Japan | 2 | 4% |
| Hungary | 1 | 2% |
| Canada | 1 | 2% |
| Switzerland | 1 | 2% |
| Brazil | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 39 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 27% |
| Other | 8 | 16% |
| Student > Ph. D. Student | 5 | 10% |
| Student > Doctoral Student | 4 | 8% |
| Student > Bachelor | 3 | 6% |
| Other | 11 | 22% |
| Unknown | 5 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 28 | 57% |
| Biochemistry, Genetics and Molecular Biology | 3 | 6% |
| Unspecified | 2 | 4% |
| Computer Science | 2 | 4% |
| Social Sciences | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 7 | 14% |