Sehgal, M. S. B.; Gondal, I.; Dooley, L. and Coppel, R.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1109/FUZZY.2006.1681940|
|Google Scholar:||Look up in Google Scholar|
Most of Gene Regulatory Network (GRN) studies are based on crisp and parametric algorithms, despite inherent fuzzy nature of gene co-regulation. This paper presents Adaptive Fuzzy Evolutionary GRN Reconstruction (AFEGRN) framework for modeling GRNs. The AFEGRN automatically determines model parameters, such as, number of clusters for fuzzy c-means using fuzzy-PBM index and Estimation of Gaussian Distribution Algorithm. The proposed strategy was tested for breast cancer and normal GRNs. The results conformed to biological knowledge and showed that most of cancer related GRN changes were caused by differentially expressed genes. This demonstrates effectiveness of AFEGRN to model any GRN.
|Item Type:||Conference Item|
|Extra Information:||ISBN: 0-7803-9488-7|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Laurence Dooley|
|Date Deposited:||10 Apr 2008|
|Last Modified:||04 Oct 2016 10:09|
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