Classification Algorithms And Analyzing The
Functionality Of Protein Families
Author(s)
L. Gao & D. K. Y. Chiu
Abstract
The rapid growth of bio-sequence data has resulted in an increasing demand for
reliable algorithms that group proteins in a meaningful way. Many traditional
classification and clustering algorithms have been adapted or directly applied to
protein sequences or structures. To capture protein functionality, new algorithms
have recently been proposed specifically aiming at incorporating protein
functions. In this paper, we review some of the classification and clustering
algorithms for proteins. We divide algorithms into four categories based on their
use of dissimilarity measure, density characteristics, computational modeling and
information of evidence. The algorithm based on information of evidence
analyzes the biomolecular sequences as discrete-valued n-tuples such that
discrete values rather than their variables are selected as evidence for the final
groupings. The advantage of this approach is that the configuration of the final
clusters does not depend on a reliable distance measure, a predefined
computational model of the clusters, reliability of the adaptive learning method
or a measure of the density function. Finally, the methods are reviewed with
respect to the quality of reflecting functionality of the protein family.
Keywords: protein families, protein functionality, classification algorithm,
clustering algorithm.
1 Introduction
Proteins are building blocks of organisms and fundamental substances of life that
play an important role in executing and regulating many biological processes.
Keywords
protein families, protein functionality, classification algorithm,clustering algorithm.
Related Book
Other papers in this volume
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