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OJBTM
Online Journal of Bioinformatics
Volume 19 (2):1235-145, 2018.
Fuzzy Neural Network
for Classification of Protein Domains into SCOP Superfamily
UB
Angadi1, M Venkatesulu2.
ABSTRACT
Angadi UB, Venkatesulu M., Fuzzy Neural
Network for Classification of Protein Domains into SCOP Superfamily.,
Onl J Bioinform., 19 (2):135-145, 2018. One
of the major research directions in bioinformatics is that of predicting the
protein superfamily in large database and classifying a given set of protein
domains in superfamilies. The classification reflects
the structural, evolutionary and functional relatedness. These relationships
are embodied in hierarchical classification such Structural Classification of
Protein (SCOP), which is manually curated. Such classification is essential for
the structural and functional analysis of proteins. A large numbers of proteins
remain unclassified and there is a greater need to develop more efficient,
accurate and automated classification methods to cope up with the speed with which
new sequences are generated. We propose an unsupervised machine learning fuzzy
neural network algorithm to classify a given set of proteins into SCOP superfamilies. The proposed method, construct a similarity
matrix from p-values of BLAST all-against-all, train the network with a simple
unsupervised max-min fuzzy neural network learning algorithm using the
similarity matrix as input vectors and finally the trained network offers SCOP
superfamily level classification. The proposed method has compared with other
techniques on different datasets and shown that the trained network is able to
classify a given set of sequences at high accuracy.
Key words: Protein classification; SCOP; fuzzy neural network; unsupervised learning.
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