Topological maps of protein sequences
type: Item
instance of: scholarly article
title: Topological maps of protein sequences
zbMATH Open document ID: 0734.92007
DOI: 10.1007/BF00204658
author: N. E. Zubov
published in: Biological Cybernetics
publication date: 1991-01-01T00:00:00Z
review text: A new method based on neural networks to cluster proteins into families is described. The network is trained with the Kohonen unsupervised learning algorithm using matrix pattern representations of the protein sequences as inputs. The components (x,y) of these 20\(\times 20\) matrix patterns are the normalized frequencies of all pairs xy of amino acids in each sequence. We investigate the influence of different learning parameters in the final topological maps obtained with a learning set of ten proteins belonging to three established families. In all cases, except in those where the synaptic vectors remains nearly unchanged during learning, the ten proteins are correctly classified into the expected families. The classification by the trained network of mutated or incomplete sequences of the learned proteins is also analyzed.
Mathematics Subject Classification ID: 92C40
Mathematics Subject Classification ID: 92D20
zbMATH DE Number: 4215764
zbMATH Keywords: homologies
zbMATH Keywords: protein database
zbMATH Keywords: neural networks
zbMATH Keywords: Kohonen unsupervised learning algorithm
zbMATH Keywords: matrix pattern representations
zbMATH Keywords: protein sequences
zbMATH Keywords: amino acids
zbMATH Keywords: topological maps
Wikidata QID: Q44497024
MaRDI profile type: Publication
cites work: Self-organized formation of topologically correct feature maps
cites work: Self-organization and associative memory.
cites work: Pattern recognition in several sequences: Consensus and alignment
description: scientific article; zbMATH DE number 4215764