booktitle = "2015 IEEE Conference on Computational Intelligence in
Bioinformatics and Computational Biology (CIBCB)",
title = "Protein secondary structure prediction using an
evolutionary computation method and clustering",
year = "2015",
abstract = "In this paper, we evaluated the performance of an
evolutionary-based protein secondary structure (PSS)
prediction model which uses the information of amino
acid sequences extracted by a clustering technique. The
dimension of the classifier's inputs is reduced using a
k-means clustering method on sequence segments. The
proposed PSS classifier is based on a Genetic
Programming (GP) approach that uses IF rules for a
multi-target classifier. The GP classifier is evaluated
by using protein sequences and the sequence information
obtained from the k-means clustering. The GP prediction
model's performance is compared with those of
feed-forward artificial neural networks (ANNs) and
support vector machines (SVMs). The prediction methods
are examined with two protein datasets RS126 and CB513.
The performance of the three classification models are
measured according to Q3 and segment overlap (SOV)
scores. The prediction models which use clustered data
result in average 2percent higher prediction accuracy
than those using sequence data. In addition, the
experimental results indicate the GP model's prediction
scores are in average 3percent higher than those of the
ANN and SVMs models when amino acid sequences or
clustered information are explored.",