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Evolved Features for DNA Sequence Classification and Their Fitness Landscapes

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2 Author(s)
Ashlock, W. ; Department ofComputer Science and Engineering, York University, Toronto, Canada ; Datta, S.

A key problem in genomics is the classification and annotation of sequences in a genome. A major challenge is identifying good sequence features. Evolutionary algorithms have the potential to search a large space of features and automatically generate useful ones. This paper proposes a two-stage method that generates features using multiple replicates of a genetic algorithm operating on an augmented finite state machine, called a side effect machine (SEM), and then selects a small diverse feature set using several methods, including a novel method called dissimilarity clustering. We apply our method to three problems related to transposable elements and compare the results to those using $k$-mer features. We are able to produce a small set of interesting and comprehensible features that create random forest classifiers more accurate and less prone to overfitting than those created using $k$-mer features. We analyze the SEM fitness landscapes and discuss the use of different fitness functions.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:17 ,  Issue: 2 )

Date of Publication:

April 2013

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