J Integr Bioinform - A new approach for modelling gene regulatory networks using fuzzy petri nets.

Tópicos

{ gene(2352) biolog(1181) express(1162) }
{ model(2656) set(1616) predict(1553) }
{ system(1976) rule(880) can(841) }
{ method(2212) result(1239) propos(1039) }
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{ data(2317) use(1299) case(1017) }
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Resumo

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.

Resumo Limpo

gene regulatori network model gene gene interact express level advent microarray technolog challeng comput scientist develop better algorithm model under regulatori relationship gene fuzzi system abil search microarray dataset activatorrepressor regulatori relationship paper present fuzzi reason model base fuzzi petri net model consid regulatori triplet mean predict chang express level target base input express level method elimin possibl fals predict classic fuzzi model therebi allow wider search space infer regulatori relationship formal fuzzi reason propos approach construct rulebas reason system experiment result show propos approach feasibl accept predict chang express level target gene

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