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require_relative "setup.rb"
class UseCasesTest < MiniTest::Test
def test_PA
# TODO add assertions
skip "This test ist very time consuming, enable on demand."
kazius = Dataset.from_sdf_file "#{DATA_DIR}/cas_4337.sdf"
hansen = Dataset.from_csv_file "#{DATA_DIR}/hansen.csv"
efsa = Dataset.from_csv_file "#{DATA_DIR}/efsa.csv"
datasets = [kazius,hansen,efsa]
map = {"1" => "mutagen", "0" => "nonmutagen"}
#p "merging"
training_dataset = Dataset.merge datasets: datasets, features: datasets.collect{|d| d.bioactivity_features.first}, value_maps: [nil,map,map], keep_original_features: false, remove_duplicates: true
assert_equal 8281, training_dataset.compounds.size
#p training_dataset.features.size
#p training_dataset.id
#training_dataset = Dataset.find('5bd8ac8fca62695d767fca6b')
#training_dataset = Dataset.find('5bd8bbadca62695f69e7a33b')
#puts training_dataset.to_csv
#p "create model_validation"
model_validation = Model::Validation.from_dataset training_dataset: training_dataset, prediction_feature: training_dataset.merged_features.first, species: "Salmonella typhimurium", endpoint: "Mutagenicity"
#p model_validation.id
#model_validation = Model::Validation.find '5bd8df47ca6269604590ab38'
#p model_validation.crossvalidations.first.predictions.select{|cid,p| !p["warnings"].empty?}
#p "predict"
pa = Dataset.from_sdf_file "#{DATA_DIR}/PA.sdf"
prediction_dataset = model_validation.predict pa
#p prediction_dataset.id
#prediction_dataset = Dataset.find('5bd98b88ca6269609aab79f4')
#puts prediction_dataset.to_csv
end
def test_tox21
# TODO add assertions
skip "This test ist very time consuming, enable on demand."
training_dataset = Dataset.from_pubchem_aid 743122
#p training_dataset.id
#'5bd9a1dbca626969d97fb421'
#File.open("AID743122.csv","w+"){|f| f.puts training_dataset.to_csv}
#model = Model::Lazar.create training_dataset: training_dataset
#p model.id
#p Model::Lazar.find('5bd9a70bca626969d97fc9df')
model_validation = Model::Validation.from_dataset training_dataset: training_dataset, prediction_feature: training_dataset.bioactivity_features.first, species: "Human HG2L7.5c1 cell line", endpoint: "aryl hydrocarbon receptor (AhR) signaling pathway activation"
#model_validation = Model::Validation.find '5bd9b210ca62696be39ab74d'
#model_validation.crossvalidations.each do |cv|
#p cv
#end
#p model_validation.crossvalidations.first.predictions.select{|cid,p| !p["warnings"].empty?}
end
def test_public_models
Download.mutagenicity
exit
#skip
# TODO clean mongo
# PubChem Classification
[
{
:aid => 1205,
:species => "Rodents",
:endpoint => "Carcinogenicity",
:qmrf => {:group => "QMRF 4.12. Carcinogenicity", :name => "OECD 451 Carcinogenicity Studies"}
},{
:aid => 1208,
:species => "Rat",
:endpoint => "Carcinogenicity",
:qmrf => {:group => "QMRF 4.12. Carcinogenicity", :name => "OECD 451 Carcinogenicity Studies"}
},{
:aid => 1199,
:species => "Mouse",
:endpoint => "Carcinogenicity",
:qmrf => {:group => "QMRF 4.12. Carcinogenicity", :name => "OECD 451 Carcinogenicity Studies"}
}
].each do |assay|
Download.pubchem_classification aid: assay[:aid], species: assay[:species], endpoint: assay[:endpoint], active: "carcinogen", inactive: "non-carcinogen", qmrf: assay[:qmrf]
end
=begin
# Mutagenicity
kazius = Dataset.from_sdf_file "#{DATA_DIR}/cas_4337.sdf"
hansen = Dataset.from_csv_file "#{DATA_DIR}/hansen.csv"
efsa = Dataset.from_csv_file "#{DATA_DIR}/efsa.csv"
datasets = [kazius,hansen,efsa]
map = {"1" => "mutagen", "0" => "nonmutagen"}
training_dataset = Dataset.merge datasets: datasets, features: datasets.collect{|d| d.bioactivity_features.first}, value_maps: [nil,map,map], keep_original_features: false, remove_duplicates: true
# rename merged feature
training_dataset.merged_features.first.name = "Mutagenicity"
training_dataset.merged_features.first.save
Model::Validation.from_dataset training_dataset: training_dataset, prediction_feature: training_dataset.merged_features.first, species: "Salmonella typhimurium", endpoint: "Mutagenicity"
# Blood Brain Barrier Penetration
bbb = Dataset.from_csv_file "#{DATA_DIR}/bbb.csv"
Model::Validation.from_dataset training_dataset: bbb, prediction_feature: bbb.bioactivity_features.first, species: "Human", endpoint: "Blood Brain Barrier Penetration"
# PubChem Regression
# TODO transformations
[
{
:aid => 1195,
:species => "Human",
:endpoint => "Maximum Recommended Daily Dose"
},{
:aid => 1208,
:species => "Rat (TD50)",
:endpoint => "Carcinogenicity"
},{
:aid => 1199,
:species => "Mouse (TD50)",
:endpoint => "Carcinogenicity"
},{
:aid => 1188,
:species => "Fathead minnow",
:endpoint => "Acute Toxicity"
}
].each do |assay|
Model::Validation.from_pubchem_aid aid: assay[:aid], species: assay[;species], endpoint: assay[:endpoint], regression:true
end
# daphnia
daphnia = Dataset.from_csv_file "#{DATA_DIR}/daphnia.csv"
Model::Validation.from_dataset training_dataset: daphnia, prediction_feature: daphnia.bioactivity_features.first, species: "Daphnia magna", endpoint: "Acute toxicity"
# LOAEL
loael = Dataset.from_csv_file "#{DATA_DIR}/loael.csv"
Model::Validation.from_dataset training_dataset: loael, prediction_feature: loael.bioactivity_features.first, species: "Rat", endpoint: "Lowest observed adverse effect level (LOAEL)"
=end
=begin
# 1204 estrogen receptor
# 1259408, # GENE-TOX
# 1159563 HepG2 cytotoxicity assay
# 588209 hepatotoxicity
# 1259333 cytotoxicity
# 1159569 HepG2 cytotoxicity counterscreen Measured in Cell-Based System Using Plate Reader - 2153-03_Inhibitor_Dose_DryPowder_Activity
# 2122 HTS Counterscreen for Detection of Compound Cytotoxicity in MIN6 Cells
# 116724 Acute toxicity determined after intravenal administration in mice
# 1148549 Toxicity in po dosed mouse assessed as mortality after 7 days
=end
end
end
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