A Petri Net-Fuzzy Predication Approach for Confidence Value of Called Genetic Bases

Abstract

We propose a novel solution for determining the confidence of a particular called genetic base within a sequence being called correctly. This has made measures of confidence of base call important and fuzzy methods have recently been used to approximate confidence by responding to data quality at the calling position. A fuzzy Petri net (FPN) approach to modeling fuzzy rule-based reasoning is proposed to determining confidence values for bases called in DNA sequencing. The proposed approach is to bring DNA bases-called within the framework of a powerful modeling tool FPN. The FPN components and functions are mapped from the different type of fuzzy operators of If-parts and Then-parts in fuzzy rules. The validation was achieved by comparing the results obtained with the FPN model and fuzzy logic using the MATLAB Toolbox; both methods have the same reasoning outcomes. Our experimental results suggest that the proposed models, can achieve the confidence values that matches, of available software.