Abstract
Finding a transcriptional regulatory network (TRN) is usually an under-determined problem. To address this challenge, we have developed a novel TRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model. A module is a set of genes with similar expression profiles, and a network represents regulatory relationships between the modules. The proposed method consists of two parts: the module selection part determines the modules with fuzzy c-mean (FCM) clustering by incorporating gene functional category information, and the network inference part uses a hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method was tested on real data from two studies: the development of rat central nervous system and the yeast cell cycle process. The results were validated with comparison to various literature sources and gene ontology biological process information.
Original language | English (US) |
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Title of host publication | Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 |
Pages | 401-407 |
Number of pages | 7 |
State | Published - 2008 |
Externally published | Yes |
Event | 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 - Las Vegas, NV, United States Duration: Jul 14 2008 → Jul 17 2008 |
Publication series
Name | Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 |
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Other
Other | 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 |
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Country/Territory | United States |
City | Las Vegas, NV |
Period | 7/14/08 → 7/17/08 |
Bibliographical note
Funding Information:Acknowledgments. This work has been supported by the Generalitat Valenciana under grant PROMETEO/2010/040, and the Spanish Administration and the FEDER Programme of the European Union under grant TEC 2008-02975/TEC.
Keywords
- Module network
- Recurrent neural network
- Swarm intelligence
- Transcriptional regulatory network