| Title |
Robust Regression Analysis of Copy Number Variation Data based on a Univariate Score
|
|---|---|
| Published in |
PLOS ONE, February 2014
|
| DOI | 10.1371/journal.pone.0086272 |
| Pubmed ID | |
| Authors |
Glen A. Satten, Andrew S. Allen, Morna Ikeda, Jennifer G. Mulle, Stephen T. Warren |
| Abstract |
The discovery that copy number variants (CNVs) are widespread in the human genome has motivated development of numerous algorithms that attempt to detect CNVs from intensity data. However, all approaches are plagued by high false discovery rates. Further, because CNVs are characterized by two dimensions (length and intensity) it is unclear how to order called CNVs to prioritize experimental validation. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Uruguay | 1 | 6% |
| Unknown | 16 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 29% |
| Student > Doctoral Student | 4 | 24% |
| Student > Ph. D. Student | 4 | 24% |
| Professor > Associate Professor | 2 | 12% |
| Student > Bachelor | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 29% |
| Biochemistry, Genetics and Molecular Biology | 3 | 18% |
| Medicine and Dentistry | 3 | 18% |
| Psychology | 2 | 12% |
| Mathematics | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 2 | 12% |