| Title |
Genome-Scale Screen for DNA Methylation-Based Detection Markers for Ovarian Cancer
|
|---|---|
| Published in |
PLOS ONE, December 2011
|
| DOI | 10.1371/journal.pone.0028141 |
| Pubmed ID | |
| Authors |
Mihaela Campan, Melissa Moffitt, Sahar Houshdaran, Hui Shen, Martin Widschwendter, Günter Daxenbichler, Tiffany Long, Christian Marth, Ite A. Laird-Offringa, Michael F. Press, Louis Dubeau, Kimberly D. Siegmund, Anna H. Wu, Susan Groshen, Uma Chandavarkar, Lynda D. Roman, Andrew Berchuck, Celeste L. Pearce, Peter W. Laird |
| Abstract |
The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 2 | 67% |
| Australia | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 9% |
| Sweden | 1 | 2% |
| Unknown | 52 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 22% |
| Student > Ph. D. Student | 12 | 21% |
| Student > Bachelor | 8 | 14% |
| Student > Doctoral Student | 4 | 7% |
| Professor > Associate Professor | 4 | 7% |
| Other | 9 | 16% |
| Unknown | 8 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 29 | 50% |
| Medicine and Dentistry | 11 | 19% |
| Biochemistry, Genetics and Molecular Biology | 9 | 16% |
| Computer Science | 1 | 2% |
| Mathematics | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 7 | 12% |