Genetic Expression Level Prediction Based on Extended Fuzzy Petri Nets
With the advances in technique for high throughput data gathering such as microarrays, DNA sequencing machines and continuous development of human genome project, the traditional physical and chemical methods have been more difficult to meet the requests of time consuming and results accuracy. Exploring and understanding the causal relationship of complex gene regulatory networks and transforming the massive data of large-scale biological research to useful biological knowledge are the present challenge. As a result, there are two typical applications both the confidence value prediction of DNA sequence and influence degree prediction of gene expression which have become the hot issues in our daily life. In this paper, two extended fuzzy Petri nets approaches are proposed, based on the existing fuzzy Petri net model, to model and analyze for the hot issues respectively. One is the fuzzy colored Petri net, which combines fuzzy Petri net with colored Petri net to model fuzzy rule-based reasoning and determine confidence values for bases called in DNA sequence. The other is extended fuzzy Petri net, which integrates reverse reasoning into fuzzy Petri net and is proposed to model gene regulatory network. It can predict the change in expression level of target based on the input expression level of activator/repressor. Compared with the method of fuzzy Petri net, the two extended fuzzy Petri nets models perform more accurately in the following typical experiment reasoning outcomes and show that the proposed methods are feasible and available.