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Identification of gene regulatory networks from time course gene expression data
- Source :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2010
- Publication Year :
- 2010
-
Abstract
- Several methods have been proposed to infer gene regulatory networks from time course gene expression data. As the number of genes is much larger than the number of time points at which gene expression (mRNA concentration) is measured, most existing methods need some ad hoc assumptions to infer a unique gene regulatory network from time course gene expression data. It is well known that gene regulatory networks are sparse and stable. However, inferred network from most existing methods may not be stable. In this paper we propose a method to infer sparse and stable gene regulatory networks from time course gene expression data. Instead of ad hoc assumption, we formulate the inference of sparse and stable gene regulatory networks as constraint optimization problems, which can be easily solved. To investigate the performance of our proposed method, computational experiments are conducted on synthetic datasets.
- Subjects :
- Proteome
Wireless ad hoc network
Computer science
Stability (learning theory)
Gene regulatory network
Inference
Computational biology
Machine learning
computer.software_genre
Models, Biological
Gene expression
Animals
Humans
Computer Simulation
Gene
Sparse matrix
Messenger RNA
business.industry
Gene Expression Profiling
Molecular biophysics
Identification (information)
ComputingMethodologies_PATTERNRECOGNITION
Gene Expression Regulation
ComputingMethodologies_GENERAL
Artificial intelligence
business
computer
Algorithms
Signal Transduction
Subjects
Details
- ISSN :
- 23757477
- Volume :
- 2010
- Database :
- OpenAIRE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Accession number :
- edsair.doi.dedup.....3498315aa10ccaadf76ed06865afff56