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In this work, the problem of classification in DNA sequences (STP Sequence-Typing Problem) is addressed. The main tool for molecular diagnosis is the polymerase chain reaction (PCR). However, the design of a PCR test is expensive (in time and economic resources) if the number of types of sequences to be classified is large and highly variable. The optimal design of PCR primers is essential to maximize the specificity and sensitivity of the test. This paper presents an algorithm to solve the problem of locating conserved sequences regions that help to discriminate between different types of hepatitis C virus using Shannon entropy concepts, information gain, a tree-based classifier and an evaluation function that considers specific parameters of PCR tests, which allow to identify patterns in conserved areas of DNA sequences.

An Algorithm to Classify DNA Sequences of Hepatitis C Virus Based on Localized Conserved Regions and Heuristic Search