Name
Title | ||
|---|---|---|
Recursive regularization for inferring gene networks from time-course gene expression profiles. |
Abstract | ||
|---|---|---|
Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.By incorporating relative importance of the VAR coefficients into the elastic net, we propose a new class of regularization, called recursive elastic net, to increase the capability of the elastic net and estimate gene networks based on the VAR model. The recursive elastic net can reduce the number of false positives gradually by updating the importance. Numerical simulations and comparisons demonstrate that the proposed method succeeds in reducing the number of false positives drastically while keeping the high number of true positives in the network inference and achieves two or more times higher true discovery rate (the proportion of true positives among the selected edges) than the competing methods even when the number of time points is small. We also compared our method with various reverse-engineering algorithms on experimental data of MCF-7 breast cancer cells stimulated with two ErbB ligands, EGF and HRG.The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1186/1752-0509-3-41 | BMC systems biology |
Keywords | Field | DocType |
variable selection,numerical simulation,bioinformatics,false positive,gene expression profiling,elastic net,systems biology,gene network,var model,gene regulatory networks,reverse engineering,vector autoregression,algorithms | Autoregressive model,Feature selection,Biology,Elastic net regularization,Multivariate statistics,Lasso (statistics),Bioinformatics,Gene regulatory network,Gene expression profiling,False positive paradox | Journal |
Volume | Issue | ISSN |
3 | 1 | 1752-0509 |
Citations | PageRank | References |
19 | 1.06 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teppei Shimamura | 1 | 38 | 8.80 |
| Seiya Imoto | 2 | 975 | 84.16 |
| Rui Yamaguchi | 3 | 180 | 26.49 |
| André Fujita | 4 | 226 | 17.14 |
| Masao Nagasaki | 5 | 368 | 26.22 |
| Satoru Miyano | 6 | 2406 | 250.71 |