Name
Title | ||
|---|---|---|
Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis. |
Abstract | ||
|---|---|---|
Motivation: The successful translation of genomic signatures into clinical settings relies on good discrimination between patient sub-groups. Many sophisticated algorithms have been proposed in the statistics and machine learning literature, but in practice simpler algorithms are often used. However, few simple algorithms have been formally described or systematically investigated. Results: We give a precise definition of a popular simple method we refer to as mas-o-menos, which calculates prognostic scores for discrimination by summing standardized predictors, weighted by the signs of their marginal associations with the outcome. We study its behavior theoretically, in simulations and in an extensive analysis of 27 independent gene expression studies of bladder, breast and ovarian cancer, altogether totaling 3833 patients with survival outcomes. We find that despite its simplicity, mas-o-menos can achieve good discrimination performance. It performs no worse, and sometimes better, than popular and much more CPU-intensive methods for discrimination, including lasso and ridge regression. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1093/bioinformatics/btu488 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Regression,Computer science,Lasso (statistics),Bioconductor,Bioinformatics,Survival analysis | Journal | 30 |
Issue | ISSN | Citations |
21 | 1367-4803 | 3 |
PageRank | References | Authors |
0.43 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sihai Dave Zhao | 1 | 3 | 0.43 |
| Giovanni Parmigiani | 2 | 174 | 12.46 |
| Curtis Huttenhower | 3 | 438 | 30.18 |
| Levi Waldron | 4 | 51 | 6.96 |