You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
| Title |
Semi-supervised consensus clustering for gene expression data analysis
|
|---|---|
| Published in |
BioData Mining, May 2014
|
| DOI | 10.1186/1756-0381-7-7 |
| Pubmed ID | |
| Authors |
Yunli Wang, Youlian Pan |
| Abstract |
Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and domain knowledge. |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 7% |
| South Africa | 1 | 2% |
| Unknown | 38 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 26% |
| Student > Ph. D. Student | 6 | 14% |
| Student > Master | 6 | 14% |
| Student > Postgraduate | 4 | 10% |
| Professor | 3 | 7% |
| Other | 5 | 12% |
| Unknown | 7 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 15 | 36% |
| Agricultural and Biological Sciences | 6 | 14% |
| Biochemistry, Genetics and Molecular Biology | 4 | 10% |
| Medicine and Dentistry | 2 | 5% |
| Business, Management and Accounting | 1 | 2% |
| Other | 5 | 12% |
| Unknown | 9 | 21% |