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Recursive regularization for inferring gene networks from time-course gene expression profiles
- Source :
- BMC Systems Biology, Vol 3, Iss 1, p 41 (2009), BMC Systems Biology
- Publisher :
- Springer Nature
-
Abstract
- Background 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. Results 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. Conclusion The recursive elastic net is a powerful tool for inferring gene networks from time-course gene expression profiles.
- Subjects :
- Elastic net regularization
Time Factors
Neuregulin-1
Gene regulatory network
Inference
Feature selection
Biology
computer.software_genre
Regularization (mathematics)
Vector autoregression
Structural Biology
Cell Line, Tumor
Modelling and Simulation
False positive paradox
Humans
Gene Regulatory Networks
lcsh:QH301-705.5
Molecular Biology
Oligonucleotide Array Sequence Analysis
Models, Genetic
Methodology Article
Gene Expression Profiling
Applied Mathematics
Computer Science Applications
lcsh:Biology (General)
Autoregressive model
Modeling and Simulation
Data mining
computer
Algorithm
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 17520509
- Volume :
- 3
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Systems Biology
- Accession number :
- edsair.doi.dedup.....132ee83c41e0ba87c39d5257b3862d98
- Full Text :
- https://doi.org/10.1186/1752-0509-3-41