Name
Abstract | ||
|---|---|---|
Genome-wide association (GWA) studies provide large amounts of high-dimensional data. GWA studies aim to identify variables that increase the risk for a given phenotype. Univariate examinations have provided some insights, but it appears that most diseases are affected by interactions of multiple factors, which can only be identified through a multivariate analysis. However, multivariate analysis on the discrete, high-dimensional and low-sample-size GWA data is made more difficult by the presence of random effects and nonspecific coupling. In this work, we investigate the suitability of three standard techniques (p-values, SVM, PCA) for analyzing GWA data on several simulated datasets. We compare these standard techniques against a sparse coding approach; we demonstrate that sparse coding clearly outperforms the other approaches and can identify interacting factors in far higherdimensional datasets than the other three approaches. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15819-3_44 | ICANN (1) |
Keywords | DocType | Volume |
feature selection,high-dimensional data,simulated datasets,higherdimensional datasets,gwa study,standard technique,multivariate analysis,genome-wide association data,genome-wide association,low-sample-size gwa data,sparse coding approach,gwa data,random effects,sample size,sparse coding,genome wide association,high dimensional data,machine learning,snp | Conference | 6352 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-15818-8 | 1 |
PageRank | References | Authors |
0.35 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ingrid Brænne | 1 | 1 | 0.69 |
| Kai Labusch | 2 | 113 | 8.50 |
| Amir Madany Mamlouk | 3 | 37 | 9.52 |