| Title |
Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery
|
|---|---|
| Published in |
PLOS ONE, November 2012
|
| DOI | 10.1371/journal.pone.0050411 |
| Pubmed ID | |
| Authors |
Sapna Kumari, Jeff Nie, Huann-Sheng Chen, Hao Ma, Ron Stewart, Xiang Li, Meng-Zhu Lu, William M. Taylor, Hairong Wei |
| Abstract |
Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. |
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 % |
|---|---|---|
| Unknown | 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 181 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 2% |
| Germany | 1 | <1% |
| Brazil | 1 | <1% |
| Netherlands | 1 | <1% |
| India | 1 | <1% |
| Sweden | 1 | <1% |
| Unknown | 172 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 62 | 34% |
| Researcher | 24 | 13% |
| Student > Master | 22 | 12% |
| Student > Postgraduate | 14 | 8% |
| Student > Bachelor | 11 | 6% |
| Other | 27 | 15% |
| Unknown | 21 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 66 | 36% |
| Biochemistry, Genetics and Molecular Biology | 43 | 24% |
| Computer Science | 21 | 12% |
| Medicine and Dentistry | 5 | 3% |
| Engineering | 5 | 3% |
| Other | 16 | 9% |
| Unknown | 25 | 14% |