An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison. Applications to the prediction of function are discussed.
%0 Journal Article
%1 j.murvai2001
%A Murvai, J.
%A Vlahovicek, K.
%A Szepesvári, Cs.
%A Pongor, S.
%D 2001
%J Genome Research
%K application bioinformatics, domain networks, neural prediction,
%N 8
%P 1410--1417
%T Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks
%U http://www.genome.org/cgi/reprint/11/8/1410.pdf
%V 11
%X An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison. Applications to the prediction of function are discussed.
@article{j.murvai2001,
abstract = {An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison. Applications to the prediction of function are discussed.},
added-at = {2020-03-17T03:03:01.000+0100},
author = {Murvai, J. and Vlahovicek, K. and Szepesv{\'a}ri, {Cs}. and Pongor, S.},
bdsk-url-1 = {http://www.genome.org/cgi/reprint/11/8/1410.pdf},
biburl = {https://www.bibsonomy.org/bibtex/29bf94f525f62bb35661719c719428e8c/csaba},
date-modified = {2010-09-02 13:09:16 -0600},
html = {http://www.genome.org/cgi/reprint/11/8/1410},
interhash = {09ade59bc68c854be986a8f2bfd5be81},
intrahash = {9bf94f525f62bb35661719c719428e8c},
journal = {Genome Research},
keywords = {application bioinformatics, domain networks, neural prediction,},
number = 8,
owner = {Beata},
pages = {1410--1417},
pdf = {papers/protdompred.pdf},
timestamp = {2020-03-17T03:03:01.000+0100},
title = {Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks},
url = {http://www.genome.org/cgi/reprint/11/8/1410.pdf},
volume = 11,
year = 2001
}