A comparison of evolved finite state classifiers and interpolated Markov models for improving PCR primer design

This presents results on training both finite state classifiers and interpolated Markov models as classifiers for polymerase chain reaction primers. The goal of the study is to find techniques to decrease the number of primers that fail to amplify correctly within a large genomics project. Standard primer design packages already select primers in a manner consistent with current knowledge of the biophysics of DNA. The classifiers trained in this effort are used to capture lab and organism specific features of primer data and are used to postprocess the output of standard primer design packages. The finite state classifiers in this study are trained with a novel evolutionary algorithm that uses an incremental fitness reward system and multipopulation hybridization. This hybridization is akin to population seeding, not the more usual hybridization of evolutionary computation with other techniques. The interpolated Markov model is a form of Markov model that adapts to data rich and data sparse portions of the training set by using a variable order in its modeling. The interpolated Markov models exhibited slightly superior performance and trains with far higher speed. The finite state classifiers provide a substantially different classification, however, and require less training data.