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OJBTM

Online Journal of Bioinformatics©

Established 1995

ISSN  1443-2250

 

Volume 23 (2):194-202, 2022.

Fuzzy neural network classification for protein structure superfamily

 

UB Angadi1, M Venkatesulu2

 

1National Institute of Animal Nutrition and Physiology, Bangalore, 2Department of Computer Applications, Kalasalingam University, Tamil Nadu, India.

 

ABSTRACT

Angadi UB, Venkatesulu M., Fuzzy neural network classification for protein structure superfamily, Onl J Bioinform, 23 (2):194-202, 2022. We describe network classification to predict protein superfamily in large databases to reflect structural, evolutionary and functional relatedness. These relationships are embodied in hierarchical classification such as structural classification of proteins (SCOP) manually curated. 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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