exp.type = "Mammalian Phenotype SemKernel Workflow"

// file containing MP URIs to train (one URI per line)
exp.trainURIsFilePath = "/tmp/semkernel/train_uris.txt"

// ontology file containing the MP ontology to derive all subclasses of the MP class to train from
exp.mpKBFilePath = "/tmp/semkernel/mp.owl"

// directory where the prepared training input data in SVM light format should be written to (one file per MP class to train)
exp.trainingInputDirectoryPath = "/tmp/semkernel/train_data"

// ontology file containing the GO ontology used to derive a semantic similarity between GO classes which is used by the SemKernel
exp.goKBFilePath = "/tmp/semkernel/go.owl"

// directory where the model data should be written to (one file per MP class to train)
exp.trainingOutputDirectoryPath = "/tmp/semkernel/models"

// file containing MP URIs to calculate a prediction for (one URI per line)
exp.predictionURIsFilePath = "/tmp/semkernel/predict_uris.txt"

// directory where the prepared prediction input data in SVM light format should be written to (one file per MP class to calculate a prediction for
exp.predictionInputDirectoryPath = "/tmp/semkernel/prediction_data"

// directory where the prediction output should be written to (one file per MP class the prediction was made for)
exp.predictionOutputDirectoryPath = "/tmp/semkernel/prediction_results"

// file containing the MGI ID to MP class mappings
// get it at http://aber-owl.net/aber-owl/diseasephenotypes/data/mousephenotypes.txt
exp.mgi2mpMappingsFilePath = "/tmp/semkernel/mousephenotypes.txt"

// file containing the MGI ID to GO class association mappings
// get it at ftp://ftp.informatics.jax.org/pub/reports/gene_association.mgi
exp.mgi2goMappingsFilePath = "/tmp/semkernel/gene_association.mgi"

// number of cross validation folds, e.g. 10 for 10-fold cross validation
exp.crossValidationFolds = 10

// the parameter C of C-SVC, epsilon-SVR, and nu-SVR
exp.cost = 5.0

// do probability prediction
exp.predictProbability = true

// ratio of positive vs negative examples
exp.posNegExampleRatio = 1.0

// determines whether the training run will be performed
exp.doTraining = true

// determines whether the prediction run will be performed
exp.doPrediction = true
