InSilicoSpectro::InSilico::RetentionTimer::Petritis Prediction of peptide retention time by neural network training
# creates a retention time predictor my $rt = InSilicoSpectro::InSilico::RetentionTimer::Petritis->new; # trains the predictor $rt->learn( data=>{expseqs=>['ELGFQG','HPGDFGADAQAAMSK','LSSPATLNSR','RFIK'], exptimes=>[1314,1194,1152,1500]},mode=>'verbose', maxepoch=>100, sqrerror=>1e-3,mode=>'verbose', nnet=>{learningrate=>0.05},layers=>[{nodes=>20},{nodes=>2},{nodes=>1}] ); # predicts retention time for a peptide $rt->predict( peptide=>'ACFGDMKWVTFISLLRPLLFSSAYSRGVFRRDTHKSEIAHRFKDLGE' ); # saves the network $rt->write_xml(confile=>'nnet01.xml'); # retrieves a previously saved network $rt->read_xml(confile=>'nnet00.xml'); # assigns a calibrator to the predictor $ec=InSilicoSpectro::InSilico::ExpCalibrator->new( fitting=>'spline' ); # fits the calibrator from expermiental values $rt->calibrate( data=>{calseqs=>['ELGFQG','HPGDFGADAQAAMSK','LSSPATLNSR','RFIK'], caltimes=>[1314,1194,1152,1500]},calibrator=>$ec ); # save current calibrator $rt->write_cal( calfile=>$file ); # retrieve previously saved calibrator $rt->read_cal ( calfile=>$file );
Predicts HPLC retention time for peptides
%h contains a hash
Trains the network from experimental data given in the arrays (@seqs,@times).
Method used for fitting
Predicts retention time for the peptide
Same as predict() but without experimental fitting
Trains the predictor with experimental data and the chosen fitting method
Filter experimental data in $rc->{data} by a cutting threshold of relative prediction error of $pc (in %).
Type of error for filtering.
Saves network into a file
Retrieves a previously saved network
Save current calibrator.
Retrieve a previously saved calibrator.
Set an instance paramter.
Get an instance parameter.
see InSilicoSpectro/t/InSilico/testPetritis.pl script
InSilicoSpectro::InSilico::RetentionTimer
InSilicoSpectro::InSilico::ExpCalibrator
Petritis K, Kangas LJ, Ferguson PL, Anderson GA, Pasa-Tolic L, Lipton MS, Auberry KJ, Strittmatter EF, Shen Y, Zhao R, Smith RD. "Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses". Anal Chem. 2003; 75(5):1039-48.
Copyright (C) 2004-2005 Geneva Bioinformatics www.genebio.com
This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
Pablo Carbonell, Alexandre Masselot, www.genebio.com
To install InSilicoSpectro, copy and paste the appropriate command in to your terminal.
cpanm
cpanm InSilicoSpectro
CPAN shell
perl -MCPAN -e shell install InSilicoSpectro
For more information on module installation, please visit the detailed CPAN module installation guide.