AFEGRN: Adaptive Fuzzy Evolutionary Gene Regulatory Network Reconstruction Framework

被引：2
|
作者：
Sehgal, Muhammad Shoaib B. [1 ]
Gondal, Iqbal [1 ]
Dooley, Laurence [1 ]
Coppel, Ross [2 ]
机构：
[1] Monash Univ, Fac Informat Technol, Clayton, Vic 3842, Australia
[2] Monash Univ, Dept Microbiol, Clayton, Vic 3842, Australia
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | 2006年
关键词：
D O I：
10.1109/FUZZY.2006.1681940
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 expressedgenes. This demonstrates effectiveness of AFEGRN to model any GRN.
引用
收藏
页码：1737 / +
页数：2
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