TY - GEN
T1 - Reverse engineering module networks by PSO-RNN hybrid modeling
AU - Zhang, Y.
AU - Xuan, J.
AU - De Los Reyes, B. G.
AU - Clarke, R.
AU - Ressom, H. W.
N1 - 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.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Module network
KW - Recurrent neural network
KW - Swarm intelligence
KW - Transcriptional regulatory network
UR - http://www.scopus.com/inward/record.url?scp=62649099295&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:62649099295
SN - 1601320558
SN - 9781601320551
T3 - Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
SP - 401
EP - 407
BT - Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
T2 - 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
Y2 - 14 July 2008 through 17 July 2008
ER -