Bioinformatics, Vol 14, 139-143, Copyright © 1998 by Oxford University Press
NA Chuzhanova, AJ Jones and S Margetts
MOTIVATION: Most of the existing methods for genetic sequence
classification are based on a computer search for homologies in nucleotide
or amino acid sequences. The standard sequence alignment programs scale
very poorly as the number of sequences increases or the degree of sequence
identity is <30%. Some new computationally inexpensive methods based on
nucleotide or amino acid compositional analysis have been proposed, but
prediction results are still unsatisfactory and depend on the features
chosen to represent the sequences. RESULTS: In this paper, a feature
selection method based on the Gamma (or near-neighbour) test is proposed.
If there is a continuous or smooth map from feature space to the
classification target values, the Gamma test gives an estimate for the
mean-squared error of the classification, despite the fact that one has no
a priori knowledge of the smooth mapping. We can search a large space of
possible feature combinations for a combination which gives a smallest
estimated mean-squared error using a genetic algorithm. The method was used
for feature selection and classification of the large subunits of rRNA
according to RDP (Ribosomal Database Project) phylogenetic classes. The
sequences were represented by dinucleotide frequency distribution. The
nearest-neighbour criterion has been used to estimate the predictive
accuracy of the classification based on the selected features. For examples
discussed, we found that the classification according to the first nearest
neighbour is correct for 80% of the test samples. If we consider the set of
the 10 nearest neighbours, then 94% of the test samples are classified
correctly. AVAILABILITY: The principal novel component of this method is
the Gamma test and this can be downloaded compiled for Unix Sun 4, Windows
95 and MS-DOS from http://www.cs.cf.ac.uk/ec/ Contact:
s.margetts@cs.cf.ac.uk
ARTICLES
Feature selection for genetic sequence classification
Institute of Mathematics, Siberian Branch of Russian Academy of Science, Novosibirsk, Russia.
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