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required parameter

advanced parameter

+RTModelTrains a model for the retention time prediction of peptides from a training set.

version1.10.0 Version of the tool that generated this parameters file.

++1Instance '1' section for 'RTModel'

in This is the name of the input file (RT prediction). It is assumed that the file type is idXML. Alternatively you can provide a .txt file having a sequence and the corresponding rt per line.
input file*.idXML,*.txt

in_positive input file with positive examples (peptide separation prediction)
input file*.idXML

in_negative input file with negative examples (peptide separation prediction)
input file*.idXML

out output file: the model in libsvm formatoutput file*.txt

svm_typeNU_SVR the type of the svm (NU_SVR or EPSILON_SVR for RT prediction, automatically set
to C_SVC for separation prediction)
NU_SVR,NU_SVC,EPSILON_SVR,C_SVC

nu0.5 the nu parameter [0..1] of the svm (for nu-SVR)0:1

p0.1 the epsilon parameter of the svm (for epsilon-SVR)

c1 the penalty parameter of the svm

kernel_typeOLIGO the kernel type of the svmLINEAR,RBF,POLY,OLIGO

degree1 the degree parameter of the kernel function of the svm (POLY kernel)
1:∞

border_length22 length of the POBK1:∞

max_std10 max standard deviation for a peptide to be included (if there are several ones for one peptide string)(median is taken)0:∞

k_mer_length1 k_mer length of the POBK1:∞

sigma5 sigma of the POBK

total_gradient_time1 the time (in seconds) of the gradient (only for RT prediction)1e-05:∞

first_dim_rtfalse if set the model will be built for first_dim_rttrue,false

additive_cvfalse if the step sizes should be interpreted additively (otherwise the actual value is multiplied
with the step size to get the new valuetrue,false

log Name of log file (created only when specified)

debug0 Sets the debug level

threads1 Sets the number of threads allowed to be used by the TOPP tool

no_progressfalse Disables progress logging to command linetrue,false

testfalse Enables the test mode (needed for internal use only)true,false

+++cvParameters for the grid search / cross validation:

skip_cvfalse Set to enable Cross-Validation or set to true if the model should just be trained with 1 set of specified parameters.true,false

number_of_runs1 number of runs for the CV (each run creates a new random partition of the data)1:∞

number_of_partitions10 number of CV partitions2:∞

degree_start1 starting point of degree1:∞

degree_step_size2 step size point of degree

degree_stop4 stopping point of degree

p_start1 starting point of p

p_step_size10 step size point of p

p_stop1000 stopping point of p

c_start1 starting point of c

c_step_size10 step size of c

c_stop1000 stopping point of c

nu_start0.3 starting point of nu0:1

nu_step_size1.2 step size of nu

nu_stop0.7 stopping point of nu0:1

sigma_start1 starting point of sigma

sigma_step_size1.3 step size of sigma

sigma_stop15 stopping point of sigma