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A novel approach to the recognition of protein architecture from sequence using fourier analysis and neural networks
Author(s) -
Shepherd Adrian J.,
Gorse Denise,
Thornton Janet M.
Publication year - 2002
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.10290
Subject(s) - artificial neural network , preprocessor , computer science , complement (music) , sequence (biology) , fourier transform , pattern recognition (psychology) , encoding (memory) , artificial intelligence , set (abstract data type) , data pre processing , algorithm , mathematics , biology , mathematical analysis , biochemistry , genetics , complementation , programming language , gene , phenotype
A novel method is presented for the prediction of protein architecture from sequence using neural networks. The method involves the preprocessing of protein sequence data by numerically encoding it and then applying a Fourier transform. The encoded and transformed data are then used to train a neural network to recognize a number of different protein architectures. The method proved significantly better than comparable alternative strategies such as percentage dipeptide frequency, but is still limited by the size of the data set and the input demands of a neural network. Its main potential is as a complement to existing fold recognition techniques, with its ability to identify global symmetries within protein structures its greatest strength. Proteins 2003;50:290–302. © 2002 Wiley‐Liss, Inc.