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|
#require_relative 'helper.rb'
require 'rdiscount'
include OpenTox
configure :production do
$logger = Logger.new(STDOUT)
enable :reloader
end
configure :development do
$logger = Logger.new(STDOUT)
enable :reloader
end
helpers do
class Numeric
def percent_of(n)
self.to_f / n.to_f * 100.0
end
end
end
before do
@version = File.read("VERSION").chomp
end
not_found do
redirect to('/predict')
end
get '/?' do
redirect to('/predict')
end
get '/predict/?' do
@models = OpenTox::Model::Validation.all
@models = @models.delete_if{|m| m.model.name =~ /\b(Net cell association)\b/}
@endpoints = @models.collect{|m| m.endpoint}.sort.uniq
@models.count <= 0 ? (haml :info) : (haml :predict)
end
get '/predict/modeldetails/:model' do
model = OpenTox::Model::Validation.find params[:model]
crossvalidations = OpenTox::Validation::RepeatedCrossValidation.find(model.repeated_crossvalidation_id).crossvalidations
return haml :model_details, :layout=> false, :locals => {:model => model, :crossvalidations => crossvalidations}
end
# get individual compound details
get '/prediction/:neighbor/details/?' do
@compound = OpenTox::Compound.find params[:neighbor]
@smiles = @compound.smiles
begin
@names = @compound.names.nil? ? "No names for this compound available." : @compound.names
rescue
@names = "No names for this compound available."
end
@inchi = @compound.inchi.gsub("InChI=", "")
haml :details, :layout => false
end
get '/jme_help/?' do
File.read(File.join('views','jme_help.html'))
end
get '/predict/dataset/:name' do
response['Content-Type'] = "text/csv"
dataset = Dataset.find_by(:name=>params[:name])
csv = dataset.to_csv
csv
end
get '/predict/:tmppath/:filename/?' do
response['Content-Type'] = "text/csv"
path = "/tmp/#{params[:tmppath]}"
send_file path, :filename => "lazar_batch_prediction_#{params[:filename]}", :type => "text/csv", :disposition => "attachment"
end
post '/predict/?' do
# process batch prediction
if !params[:fileselect].blank?
if params[:fileselect][:filename] !~ /\.csv$/
@error_report = "Please submit a csv file."
return haml :error
end
File.open('tmp/' + params[:fileselect][:filename], "w") do |f|
f.write(params[:fileselect][:tempfile].read)
end
@filename = params[:fileselect][:filename]
begin
input = OpenTox::Dataset.from_csv_file File.join("tmp", params[:fileselect][:filename]), true
if input.class == OpenTox::Dataset
dataset = OpenTox::Dataset.find input
else
@error_report = "Could not serialize file '#{@filename}' ."
return haml :error
end
rescue
@error_report = "Could not serialize file '#{@filename}' ."
return haml :error
end
@compounds = dataset.compounds
if @compounds.size == 0
@error_report = dataset[:warnings]
dataset.delete
return haml :error
end
# for csv export
@batch = {}
# for haml table
@view = {}
@compounds.each{|c| @view[c] = []}
params[:selection].keys.each do |model_id|
model = OpenTox::Model::Validation.find model_id
@batch[model] = []
@compounds.each_with_index do |compound,idx|
prediction = model.predict(compound)
@batch[model] << [compound, prediction]
@view[compound] << [model,prediction]
end
end
@csvhash = {}
@warnings = dataset[:warnings]
dupEntries = {}
delEntries = ""
# split duplicates and deleted entries
@warnings.each do |w|
substring = w.match(/line .* of/)
unless substring.nil?
delEntries += "\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"#{w.sub(/\b(tmp\/)\b/,"")}\"\n"
end
substring = w.match(/rows .* Entries/)
unless substring.nil?
lines = []
substring[0].split(",").each{|s| lines << s[/\d+/]}
lines.shift
lines.each{|l| dupEntries[l.to_i] = w.split(".").first}
end
end
@batch.each_with_index do |hash, idx|
@csvhash[idx] = ""
model = hash[0]
values = hash[1]
dupEntries.keys.each{|k| values.insert(k-1, dupEntries[k])}.compact!
values.each_with_index do |array, id|
unless array.kind_of? String
compound = array[0]
prediction = array[1]
smiles = compound.smiles
type = model.model.class.to_s.match("Classification") ? "Classification" : "Regression"
endpoint = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
pred = propA = propB = interval = inApp = inT = note = ""
if prediction[:neighbors]
if prediction[:value]
pred = prediction[:value].numeric? ? "#{prediction[:value].delog10.signif(3)} (#{model.unit}), #{compound.mmol_to_mg(prediction[:value].delog10.signif(3))} #{(model.unit =~ /\b(mol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : prediction[:value]
int = (prediction[:prediction_interval].nil? ? nil : prediction[:prediction_interval])
interval = (int.nil? ? "" : "#{int[1].delog10.signif(3)} - #{int[0].delog10.signif(3)} (#{model.unit})")
inApp = "yes"
inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
unless prediction[:probabilities].nil?
if id == 0
probFirst = probLast = ""
probFirst = prediction[:probabilities].keys.first.capitalize
prediction[:probabilities].keys.last.split("-").each{|s| probLast += s.capitalize}
@csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction\",\"predProbability#{probFirst}\",\"predProbability#{probLast}\",\"95% Prediction interval\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
unless delEntries.blank? and id == 0
@csvhash[idx] += delEntries
end
end
propA = "#{prediction[:probabilities].values_at(prediction[:probabilities].keys.first)[0].to_f.signif(3)}"
propB = "#{prediction[:probabilities].values_at(prediction[:probabilities].keys.last)[0].to_f.signif(3)}"
else
@csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction\",\"predProbability\",\"predProbability\",\"95% Prediction interval\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
unless delEntries.blank? and id == 0
@csvhash[idx] += delEntries
end
end
# only one neighbor
else
inApp = "no"
inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
end
else # no prediction value
inApp = "no"
inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
end
if @warnings
@warnings.each do |w|
note += (w.split(".").first + ".") if /\b(#{Regexp.escape(smiles)})\b/ === w
end
end
else
endpoint = type = smiles = pred = propA = propB = interval = inApp = inT = ""
note = array
end
@csvhash[idx] += "\"#{id+1}\",\"#{endpoint}\",\"#{type}\",\"#{smiles}\",\"#{pred}\",\"#{propA}\",\"#{propB}\",\"#{interval}\",\"#{inApp}\",\"#{inT}\",\"#{note.chomp}\"\n"
end
end
t = Tempfile.new
@csvhash.each do |model, csv|
t.write(csv)
t.write("\n")
end
t.rewind
@tmppath = t.path.split("/").last
dataset.delete
File.delete File.join("tmp", params[:fileselect][:filename])
return haml :batch
end
# validate identifier input
if !params[:identifier].blank?
@identifier = params[:identifier]
$logger.debug "input:#{@identifier}"
# get compound from SMILES
@compound = Compound.from_smiles @identifier
if @compound.blank?
@error_report = "'#{@identifier}' is not a valid SMILES string."
return haml :error
end
@models = []
@predictions = []
params[:selection].keys.each do |model_id|
model = OpenTox::Model::Validation.find model_id
@models << model
@predictions << model.predict(@compound)
end
haml :prediction
end
end
get "/report/:id/?" do
lazarpath = `gem path lazar`
lazarpath = File.dirname lazarpath
lazarpath = File.dirname lazarpath
qmrfpath = `gem path qsar-report`
qmrfpath = File.dirname qmrfpath
qmrfpath = File.dirname qmrfpath
prediction_model = Model::Validation.find params[:id]
model = prediction_model.model
validation_template = "./views/model_details.haml"
if File.directory?(lazarpath)
lazar_commit = `cd #{lazarpath}; 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 => "Maunz, Guetlein, Rautenberg, Vorgrimmler, Gebele and Helma (2013), 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
# Reference(s) to main scientific papers and/or software package 2.7
report.change_catalog :publications_catalog, :publications_catalog_2, {:title => "Maunz A and Helma C (2008) Prediction of chemical toxicity with local support vector regression and activity-specific kernels. SAR & QSAR in Environmental Research 19 (5-6), 413-431", :url => "http://dx.doi.org/10.1080/10629360802358430"}
report.ref_catalog :references, :publications_catalog, :publications_catalog_2
# 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}"
model_type = model.class.to_s.gsub('OpenTox::Model::Lazar','')
# Type of model 4.1
report.value "algorithm_type", "#{model_type}"
# 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 => "Neighbor algorithm: #{model.algorithms["similarity"]["method"].gsub('_',' ').titleize}#{(model.algorithms["similarity"][:min] ? ' with similarity > ' + model.algorithms["similarity"][:min].to_s : '')}"}
report.ref_catalog :algorithm_explicit, :algorithms_catalog, :algorithms_catalog_1
report.change_catalog :algorithms_catalog, :algorithms_catalog_3, {:definition => "see Helma 2016 and lazar.in-silico.ch, submitted version: #{lazar_commit}", :description => "modified k-nearest neighbor #{model_type}"}
report.ref_catalog :algorithm_explicit, :algorithms_catalog, :algorithms_catalog_3
if model.algorithms["prediction"]
pred_algorithm_params = (model.algorithms["prediction"][:method] == "rf" ? "random forest" : model.algorithms["prediction"][:method])
end
report.change_catalog :algorithms_catalog, :algorithms_catalog_2, {:definition => "see Helma 2016 and lazar.in-silico.ch, submitted version: #{lazar_commit}", :description => "Prediction algorithm: #{model.algorithms["prediction"].to_s.gsub('OpenTox::Algorithm::','').gsub('_',' ').gsub('.', ' with ')} #{(pred_algorithm_params ? pred_algorithm_params : '')}"}
report.ref_catalog :algorithm_explicit, :algorithms_catalog, :algorithms_catalog_2
# Descriptors in the model 4.3
if model.algorithms["descriptors"][:type]
report.change_catalog :descriptors_catalog, :descriptors_catalog_1, {:description => "", :name => "#{model.algorithms["descriptors"][:type]}", :publication_ref => "", :units => ""}
report.ref_catalog :algorithms_descriptors, :descriptors_catalog, :descriptors_catalog_1
end
# Descriptor selection 4.4
report.value "descriptors_selection", "#{model.algorithms["feature_selection"].gsub('_',' ')} #{model.algorithms["feature_selection"].collect{|k,v| k.to_s + ': ' + v.to_s}.join(', ')}" if model.algorithms["feature_selection"]
# 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")
# Robustness - Statistics obtained by leave-many-out cross-validation 6.9
if prediction_model.repeated_crossvalidation
$logger.error "#####################{prediction_model}"
crossvalidations = prediction_model.crossvalidations
out = haml File.read(validation_template), :layout=> false, :locals => {:model => prediction_model, :crossvalidations => crossvalidations}
report.value "lmo", out
end
# Mechanistic basis of the model 8.1
report.value "mechanistic_basis","<html><head></head><body>
<p>
Compounds with similar structures (neighbors) are assumed to have
similar activities as the query compound. For the determination of
activity specific similarities only statistically relevant subtructures
(paths) are used. For this reason there is a priori no bias towards
specific mechanistic hypothesis.
</p>
</body>
</html>"
# A priori or a posteriori mechanistic interpretation 8.2
report.value "mechanistic_basis_comments","a posteriori for individual predictions"
# Other information about the mechanistic interpretation 8.3
report.value "mechanistic_basis_info","<html><head></head><body><p>Hypothesis about biochemical mechanisms can be derived from individual
predictions by inspecting neighbors and relevant fragments.</p>
<p>Neighbors are compounds that are similar in respect to a certain
endpoint and it is likely that compounds with high similarity act by
similar mechanisms as the query compound. Links at the webinterface
prove an easy access to additional experimental data and literature
citations for the neighbors and the query structure.</p>
<p>Activating and deactivating parts of the query compound are highlighted
in red and green on the webinterface. Fragments that are unknown (or too
infrequent for statistical evaluation are marked in yellow and
additional statistical information about the individual fragments can be
retrieved. Please note that lazar predictions are based on neighbors and
not on fragments. Fragments and their statistical significance are used
for the calculation of activity specific similarities.</p>"
# Bibliography 9.2
report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_1
report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_2
report.change_catalog :publications_catalog, :publications_catalog_3, {:title => "Helma (2006), Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity.", :url => "http://dx.doi.org/10.1007/s11030-005-9001-5"}
report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_3
# output
t = Tempfile.new
t << report.to_xml
send_file t.path, :filename => "QMRF_report_#{model.name}.xml", :type => "application/xml", :disposition => "attachment"
end
get '/license' do
@license = RDiscount.new(File.read("LICENSE.md")).to_html
haml :license, :layout => false
end
get '/faq' do
@faq = RDiscount.new(File.read("FAQ.md")).to_html
haml :faq, :layout => false
end
get '/style.css' do
headers 'Content-Type' => 'text/css; charset=utf-8'
scss :style
end
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