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2017 | OriginalPaper | Chapter

Feature Reduced Weighted Fuzzy Binarization for Histogram Comparison of Promoter Sequences

Authors : K. Kouser, Lalitha Rangarajan

Published in: Recent Trends in Image Processing and Pattern Recognition

Publisher: Springer Singapore

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Abstract

Effective biological sequence analysis methods are in great demand. This is due to the increasing amount of sequence data being generated from the improved sequencing techniques. In this study, we select statistically significant features/motifs from the Position Specific Motif Matrices of promoters. Later, we reconstruct these matrices using the chosen motifs. The reconstructed matrices are then binarized using triangular fuzzy membership values. Then the binarized matrix is assigned weights to obtain the texture features. Histogram is plotted to visualize the distribution of texture values of each promoter and later histogram difference is computed across pairs of promoters. This histogram difference is a measure of underlying dissimilarity in the promoters being compared. A dissimilarity matrix is constructed using the histogram difference values of all the promoter pairs. From the experiments, the combination of feature reduction and fuzzy binarization seems to be useful in promoter differentiation.

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Metadata
Title
Feature Reduced Weighted Fuzzy Binarization for Histogram Comparison of Promoter Sequences
Authors
K. Kouser
Lalitha Rangarajan
Copyright Year
2017
Publisher
Springer Singapore
Book
Recent Trends in Image Processing and Pattern Recognition

Print ISBN: 978-981-10-4858-6

Electronic ISBN: 978-981-10-4859-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-4859-3_16

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