We propose a neural net solution for the recognition of the domain types of proteins, which is a hard and important problem in biology. We have found that using a clever preprocessing technique relatively small neural networks perform surprisingly well. The performances of the neural nets were measured by cross-validation and Hoeffding's inequality was utilized for the estimation of a confidence interval of the estimates.
%0 Report
%1 j.murvai1997
%A Murvai, J.
%A Szepesvári, Cs.
%A Bachrati, Cs.
%A Pongor, S.
%C Szeged, HU-6700
%D 1997
%K application bioinformatics, domain networks, neural prediction,
%N TR-98-117
%T Prediction of Protein Domain-Types by Backpropagation
%X We propose a neural net solution for the recognition of the domain types of proteins, which is a hard and important problem in biology. We have found that using a clever preprocessing technique relatively small neural networks perform surprisingly well. The performances of the neural nets were measured by cross-validation and Hoeffding's inequality was utilized for the estimation of a confidence interval of the estimates.
@techreport{j.murvai1997,
abstract = {We propose a neural net solution for the recognition of the domain types of proteins, which is a hard and important problem in biology. We have found that using a clever preprocessing technique relatively small neural networks perform surprisingly well. The performances of the neural nets were measured by cross-validation and Hoeffding's inequality was utilized for the estimation of a confidence interval of the estimates.},
added-at = {2020-03-17T03:03:01.000+0100},
address = {Szeged, HU-6700},
author = {Murvai, J. and Szepesv{\'a}ri, {Cs}. and Bachrati, Cs. and Pongor, S.},
biburl = {https://www.bibsonomy.org/bibtex/222d371491e09876184c0dea968ab8a4b/csaba},
date-modified = {2010-09-02 13:09:16 -0600},
institution = {``Attila J{\'o}zsef'' University, Research Group on Artificial Intelligence},
interhash = {e74aeb8d7721bbe4fe9f03a7c3886b17},
intrahash = {22d371491e09876184c0dea968ab8a4b},
keywords = {application bioinformatics, domain networks, neural prediction,},
number = {TR-98-117},
owner = {Beata},
pdf = {papers/RGAI-98-117.ps.pdf},
timestamp = {2020-03-17T03:03:01.000+0100},
title = {Prediction of Protein Domain-Types by Backpropagation},
year = 1997
}