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RTModel

Used to train a prediction model for peptide retention time prediction or peptide separation prediction.

For retention time prediction, a support vector machine is trained with peptide sequences and their measured retention times. For peptide separation prediction two files have to be given. One file contains the positive examples (the peptides which are collected) and one file contains the negative peptides (the flowthrough peptides).

There are a number of parameters which can be changed for the svm (specified in the ini file):


The last five parameters (sigma, degree, c, nu and p) can be used in a cross validation (CV) to find the best parameters according to the training set. Therefore you have to specify the start value of a parameter, the step size in which the parameters should be increased and a final value for the particular parameter such that the tested parameter is never bigger than the given final value. If you want to perform a cross validation for example for the parameter c, you have to specify c_start, c_step_size and c_stop in the ini file. So if you want to perform a CV for c from 0.1 to 2 with step size 0.1 you include the following lines into your ini-file:

If the CV should test additional parameters in a certain range you just include them analogously to the example above. Furthermore, you can specify the number of partitions for the CV with number_of_partitions in the ini file and the number of runs with number_of_runs.


Consequently you have two choices to use this application:

  1. Set the parameters of the svm: The RTModel application will train the svm with the training data and store the svm model
  2. Give a range of parameters for which a CV should be performed: The RTModel application will perform a CV to find the best parameter combination in the given range and afterwards train the svm with the best parameters and the whole training data. Then the model is stored.


The model can be used in RTPredict, to predict retention times for peptides or peptide separation depending on how you trained the model.


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