Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition.
In this paper we present a study of classification of the 20 amino acids via a fuzzy clustering technique. In order to calculate distances among the various elements we employ two different distance functions: the Minkowski distance function and the NTV metric. In the clustering procedure we take into account several physical properties of the amino acids. We examine the effect of the number and nature of properties taken into account to the clustering procedure as a function of the degree of similarity and the distance function used. It turns out that one should use the properties that determine in the more important way the behavior of the amino acids and that the use of the appropriate metric can help in defining the separation into groups.
Georgiou
D N
DN
Department of Mathematics, University of Patras, 265 00 Patras, Greece.
Karakasidis
T E
TE
Nieto
J J
JJ
Torres
A
A
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry / Q000145:classification*; D000818:Animals; D002627:Chemistry, Physical; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D017143:Fuzzy Logic; D057927:Hydrophobic and Hydrophilic Interactions; D008956:Models, Chemical; D008969:Molecular Sequence Data; D008970:Molecular Weight
D000596:Amino Acids