A new approach for modelling gene regulatory networks using fuzzy petri nets.
Gene Regulatory Networks are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rulebased reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.
Department of Computer Science, JMI University, New Delhi 110025, India. raed.inf@gmail.com
Raed I
RI
Hamed
S I
SI
Ahson
R
R
Parveen
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D046228:Microarray Analysis