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# Get a list of all possible reports to prediction models
# @param [Header] Accept one of text/uri-list,
# @return [text/uri-list] list of all prediction models
get "/report/?" do
models = Model::Prediction.all
case @accept
when "text/uri-list"
uri_list = models.collect{|model| uri("/report/#{model.model_id}")}
return uri_list.join("\n") + "\n"
when "application/json"
reports = [{}]
#models = JSON.parse models.to_json
models.each_index do |idx|
reports[idx] = {}
reports[idx][:URI] = uri("/report/#{models[idx]["model_id"]}")
reports[idx][:repeated_crossvalidation_uri] = uri("/validation/repeatedcrossvalidation/#{models[idx]["repeated_crossvalidation_id"]}") if models[idx]["repeated_crossvalidation_id"]
reports[idx][:leave_one_out_validation_uri] = uri("/validation/leaveoneoutvalidation/#{models[idx]["leave_one_out_validation_id"]}") if models[idx]["leave_one_out_validation_id"]
reports[idx][:training_dataset_URI] = uri("/dataset/#{models[idx].training_dataset.id}") if models[idx].training_dataset.id
end
return reports.to_json
else
bad_request_error "Mime type #{@accept} is not supported."
end
end
get "/report/:id/?" do
model = Model::Lazar.find params[:id]
resource_not_found_error "Model with id: #{params[:id]} not found." unless model
prediction_model = Model::Prediction.find_by :model_id => params[:id]
validation_template = File.join(File.dirname(__FILE__),"../views/model_details.haml")
if File.directory?("#{File.dirname(__FILE__)}/../../lazar")
lazar_commit = `cd #{File.dirname(__FILE__)}/../../lazar; git rev-parse HEAD`.strip
lazar_commit = "https://github.com/opentox/lazar/tree/#{lazar_commit}"
else
lazar_commit = "https://github.com/opentox/lazar/releases/tag/v#{Gem.loaded_specs["lazar"].version}"
end
report = OpenTox::QMRFReport.new
# QSAR Identifier Title 1.1
report.value "QSAR_title", "Lazar model for #{prediction_model.species} #{prediction_model.endpoint}"
# Software coding the model 1.3
report.change_catalog :software_catalog, :firstsoftware, {:name => "lazar", :description => "lazar Lazy Structure- Activity Relationships", :number => "1", :url => "https://lazar.in-silico.ch", :contact => "info@in-silico.ch"}
report.ref_catalog :QSAR_software, :software_catalog, :firstsoftware
# Date of QMRF 2.1
report.value "qmrf_date", "#{Time.now.strftime('%d %B %Y')}"
# QMRF author(s) and contact details 2.1
report.change_catalog :authors_catalog, :firstauthor, {:name => "Christoph Helma", :affiliation => "in silico toxicology gmbh", :contact => "Rastatterstr. 41, CH-4057 Basel", :email => "info@in-silico.ch", :number => "1", :url => "www.in-silico.ch"}
report.ref_catalog :qmrf_authors, :authors_catalog, :firstauthor
# Model developer(s) and contact details 2.5
report.change_catalog :authors_catalog, :modelauthor, {:name => "Christoph Helma", :affiliation => "in silico toxicology gmbh", :contact => "Rastatterstr. 41, CH-4057 Basel", :email => "info@in-silico.ch", :number => "1", :url => "www.in-silico.ch"}
report.ref_catalog :model_authors, :authors_catalog, :modelauthor
# Date of model development and/or publication 2.6
report.value "model_date", "#{Time.parse(model.created_at.to_s).strftime('%Y')}"
# Reference(s) to main scientific papers and/or software package 2.7
report.change_catalog :publications_catalog, :publications_catalog_1, {:title => "lazar: a modular predictive toxicology framework", :url => "http://dx.doi.org/10.3389/fphar.2013.00038"}
report.ref_catalog :references, :publications_catalog, :publications_catalog_1
# Species 3.1
report.value "model_species", prediction_model.species
# Endpoint 3.2
report.change_catalog :endpoints_catalog, :endpoints_catalog_1, {:name => prediction_model.endpoint, :group => ""}
report.ref_catalog :model_endpoint, :endpoints_catalog, :endpoints_catalog_1
# Endpoint Units 3.4
report.value "endpoint_units", "#{prediction_model.unit}"
# Type of model 4.1
report.value "algorithm_type", "#{model.class.to_s.gsub('OpenTox::Model::Lazar','')}"
# Explicit algorithm 4.2
report.change_catalog :algorithms_catalog, :algorithms_catalog_1, {:definition => "see Helma 2016 and lazar.in-silico.ch, submitted version: #{lazar_commit}", :description => "modified k-nearest neighbor classification with activity specific similarities, weighted voting and exhaustive enumeration of fragments and neighbors"}
report.ref_catalog :algorithm_explicit, :algorithms_catalog, :algorithms_catalog_1
# Descriptors in the model 4.3
report.change_catalog :descriptors_catalog, :descriptors_catalog_1, {:description => "all statistically relevant paths are used for similarity calculation", :name => "linear fragmens (paths)", :publication_ref => "", :units => "true/false (i.e. present/absent)"}
report.ref_catalog :algorithms_descriptors, :descriptors_catalog, :descriptors_catalog_1
# Descriptor selection 4.4
report.value "descriptors_selection", "statistical filter (chi-square with Yates correction)"
# Algorithm and descriptor generation 4.5
report.value "descriptors_generation", "exhaustive breadth first search for paths in chemical graphs (simplified MolFea algorithm)"
# Software name and version for descriptor generation 4.6
report.change_catalog :software_catalog, :software_catalog_2, {:name => "lazar, submitted version: #{lazar_commit}", :description => "simplified MolFea algorithm", :number => "2", :url => "https://lazar.in-silico.ch", :contact => "info@in-silico.ch"}
report.ref_catalog :descriptors_generation_software, :software_catalog, :software_catalog_2
# Chemicals/Descriptors ratio 4.7
report.value "descriptors_chemicals_ratio", "not applicable (classification based on activities of neighbors, descriptors are used for similarity calculation)"
# Description of the applicability domain of the model 5.1
report.value "app_domain_description", "<html>
<head>
</head>
<body>
<p>
The applicability domain (AD) of the training set is characterized by
the confidence index of a prediction (high confidence index: close to
the applicability domain of the training set/reliable prediction, low
confidence: far from the applicability domain of the
trainingset/unreliable prediction). The confidence index considers (i)
the similarity and number of neighbors and (ii) contradictory examples
within the neighbors. A formal definition can be found in Helma 2006.
</p>
<p>
The reliability of predictions decreases gradually with increasing
distance from the applicability domain (i.e. decreasing confidence index)
</p>
</body>
</html>"
# Method used to assess the applicability domain 5.2
report.value "app_domain_method", "see Helma 2006 and Maunz 2008"
# Software name and version for applicability domain assessment 5.3
report.change_catalog :software_catalog, :software_catalog_3, {:name => "lazar, submitted version: #{lazar_commit}", :description => "integrated into main lazar algorithm", :number => "3", :url => "https://lazar.in-silico.ch", :contact => "info@in-silico.ch"}
report.ref_catalog :app_domain_software, :software_catalog, :software_catalog_3
# Limits of applicability 5.4
report.value "applicability_limits", "Predictions with low confidence index, unknown substructures and neighbors that might act by different mechanisms"
# Availability of the training set 6.1
report.change_attributes "training_set_availability", {:answer => "Yes"}
# Available information for the training set 6.2
report.change_attributes "training_set_data", {:cas => "Yes", :chemname => "Yes", :formula => "Yes", :inchi => "Yes", :mol => "Yes", :smiles => "Yes"}
# Data for each descriptor variable for the training set 6.3
report.change_attributes "training_set_descriptors", {:answer => "No"}
# Data for the dependent variable for the training set 6.4
report.change_attributes "dependent_var_availability", {:answer => "All"}
# Other information about the training set 6.5
report.value "other_info", "#{prediction_model.source}"
# Pre-processing of data before modelling 6.6
report.value "preprocessing", (model.class == OpenTox::Model::LazarRegression ? "-log10 transformation" : "none")
if prediction_model.crossvalidations
crossvalidations = prediction_model.crossvalidations
out = haml File.read(validation_template), :layout=> false, :locals => {:model => prediction_model}
report.value "lmo", out
end
# output
response['Content-Type'] = "application/xml"
return report.to_xml
end
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