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author | gebele <gebele@in-silico.ch> | 2016-11-10 11:06:27 +0000 |
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committer | gebele <gebele@in-silico.ch> | 2016-11-10 11:06:27 +0000 |
commit | 295dcfc74e1375e495ec3d9c1e74a402eb4decd4 (patch) | |
tree | d819b96db3cade6958f0569d161d39906fcd61c4 /lib/model.rb | |
parent | 461a77d91054bd5456f6a1e2b5c7ed30aac56d43 (diff) |
added nanomodel create
Diffstat (limited to 'lib/model.rb')
-rw-r--r-- | lib/model.rb | 22 |
1 files changed, 22 insertions, 0 deletions
diff --git a/lib/model.rb b/lib/model.rb index 7503215..adcbcc6 100644 --- a/lib/model.rb +++ b/lib/model.rb @@ -418,6 +418,28 @@ module OpenTox prediction_model end + def self.create dir: dir, algorithms: algorithms + training_dataset = Dataset.where(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first + unless training_dataset + Import::Enanomapper.import dir + training_dataset = Dataset.where(name: "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first + end + prediction_model = self.new( + :endpoint => "log2(Net cell association)", + :source => "https://data.enanomapper.net/", + :species => "A549 human lung epithelial carcinoma cells", + :unit => "log2(ug/Mg)" + ) + prediction_feature = Feature.where(name: "log2(Net cell association)", category: "TOX").first + model = Model::LazarRegression.create(prediction_feature: prediction_feature, training_dataset: training_dataset, algorithms: algorithms) + prediction_model[:model_id] = model.id + repeated_cv = Validation::RepeatedCrossValidation.create model + prediction_model[:repeated_crossvalidation_id] = Validation::RepeatedCrossValidation.create(model).id + #prediction_model[:leave_one_out_validation_id] = Validation::LeaveOneOut.create(model).id + prediction_model.save + prediction_model + end + end end |