Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.
R. Xu et al., "Inference of Genetic Regulatory Networks with Recurrent Neural Network Models," Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004, Institute of Electrical and Electronics Engineers (IEEE), Jan 2004.
The definitive version is available at https://doi.org/10.1109/IEMBS.2004.1403826
26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004
Electrical and Computer Engineering
Keywords and Phrases
Backpropagation; Biochemistry; Biology Computing; Cellular Biophysics; Fundamental Cellular Process; Gene Function; Gene Interactions; Gene Relations; Genetic Regulatory Networks; Genetics; Molecular Biophysics; Recurrent Neural Nets; Recurrent Neural Network Models; Time Series; Time Series Gene Expression
Article - Conference proceedings
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2004