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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]
# create header
if model.regression?
predAunit = "(#{model.unit})"
predBunit = "(#{model.unit =~ /mmol\/L/ ? "(mol/L)" : "(mg/kg_bw/day)"})"
@csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction #{predAunit}\",\"Prediction #{predBunit}\",\"95% Prediction interval (low) #{predAunit}\",\"95% Prediction interval (high) #{predAunit}\",\"95% Prediction interval (low) #{predBunit}\",\"95% Prediction interval (high) #{predBunit}\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
else #classification
av = model.prediction_feature.accept_values
probFirst = av[0].capitalize
probLast = av[1].capitalize
@csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction\",\"predProbability#{probFirst}\",\"predProbability#{probLast}\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
end
values = hash[1]
dupEntries.keys.each{|k| values.insert(k-1, dupEntries[k])}.compact!
values.each_with_index do |array, id|
type = (model.regression? ? "Regression" : "Classification")
endpoint = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
if id == 0
@csvhash[idx] += delEntries unless delEntries.blank?
end
unless array.kind_of? String
compound = array[0]
prediction = array[1]
smiles = compound.smiles
if prediction[:neighbors]
if prediction[:value]
pred = prediction[:value].numeric? ? "#{prediction[:value].delog10.signif(3)}" : prediction[:value]
predA = prediction[:value].numeric? ? "#{prediction[:value].delog10.signif(3)}" : prediction[:value]
predAunit = prediction[:value].numeric? ? "(#{model.unit})" : ""
predB = prediction[:value].numeric? ? "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)}" : prediction[:value]
predBunit = prediction[:value].numeric? ? "#{model.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
int = (prediction[:prediction_interval].nil? ? nil : prediction[:prediction_interval])
intervalLow = (int.nil? ? "" : "#{int[1].delog10.signif(3)}")
intervalHigh = (int.nil? ? "" : "#{int[0].delog10.signif(3)}")
intervalLowMg = (int.nil? ? "" : "#{compound.mmol_to_mg(int[1].delog10).signif(3)}")
intervalHighMg = (int.nil? ? "" : "#{compound.mmol_to_mg(int[0].delog10).signif(3)}")
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?
av = model.prediction_feature.accept_values
propA = "#{prediction[:probabilities][av[0]].to_f.signif(3)}"
propB = "#{prediction[:probabilities][av[1]].to_f.signif(3)}"
end
else
# no prediction value only one neighbor
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
# string note for duplicates
endpoint = type = smiles = pred = predA = predB = propA = propB = intervalLow = intervalHigh = intervalLowMg = intervalHighMg = inApp = inT = ""
note = array
end
if model.regression?
@csvhash[idx] += "\"#{id+1}\",\"#{endpoint}\",\"#{type}\",\"#{smiles}\",\"#{predA}\",\"#{predB}\",\"#{intervalLow}\",\"#{intervalHigh}\",\"#{intervalLowMg}\",\"#{intervalHighMg}\",\"#{inApp}\",\"#{inT}\",\"#{note.chomp}\"\n"
else
@csvhash[idx] += "\"#{id+1}\",\"#{endpoint}\",\"#{type}\",\"#{smiles}\",\"#{pred}\",\"#{propA}\",\"#{propB}\",\"#{inApp}\",\"#{inT}\",\"#{note.chomp}\"\n"
end
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.downcase}"
# Software coding the model 1.3
report.change_catalog :software_catalog, :firstsoftware, {:name => "lazar", :description => "lazar Lazy Structure- Activity Relationships. See #{lazar_commit}", :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 A., Guetlein M., Rautenberg M., Vorgrimmler D., Gebele D. and Helma C. (2013), lazar: a modular predictive toxicology framework ", :number => "1", :url => "http://dx.doi.org/10.3389/fphar.2013.00038"}
report.ref_catalog :references, :publications_catalog, :publications_catalog_1
report.change_catalog :publications_catalog, :publications_catalog_2, {:title => "Helma C, Gebele D, Rautenberg M (2017) lazar, software available at https://lazar.in-silico.ch,source code available at #{lazar_commit}", :number => "2", :url => "https://doi.org/10.5281/zenodo.215483"}
report.ref_catalog :references, :publications_catalog, :publications_catalog_2
# Availability of information about the model 2.8
report.value "info_availability", "Prediction interface and validation results available at https://lazar.in-silico.ch"
# Species 3.1
report.value "model_species", prediction_model.species
# Endpoint 3.2
report.change_catalog :endpoints_catalog, :endpoints_catalog_1, {:name => prediction_model.qmrf["name"], :group => "#{prediction_model.qmrf["group"]}"}
report.ref_catalog :model_endpoint, :endpoints_catalog, :endpoints_catalog_1
# Endpoint Units 3.4
report.value "endpoint_units", "#{prediction_model.unit}"
# Dependent variable 3.5
report.value "endpoint_variable", "#{prediction_model.endpoint} #{prediction_model.regression? ? "regression" : "classification"}"
# Type of model 4.1
model_type = model.class.to_s.gsub('OpenTox::Model::Lazar','')
report.value "algorithm_type", "#{model_type}"
# Explicit algorithm 4.2
report.ref_catalog :algorithm_explicit, :algorithms_catalog, :algorithms_catalog_1
report.change_catalog :algorithms_catalog, :algorithms_catalog_1, {:definition => "", :description => "modified k-nearest neighbor #{model_type.downcase} (#{model_type =~ /regression/i ? "local random forest" : "weighted majority vote"}), see #{lazar_commit}" }
# Descriptors in the model 4.3
if model.algorithms["descriptors"][:type]
report.change_catalog :descriptors_catalog, :descriptors_catalog_1, {:description => "Molprint 2D (Bender et al. 2004)", :name => "#{model.algorithms["descriptors"][:type]} fingerprints", :publication_ref => "", :units => ""}
report.ref_catalog :algorithms_descriptors, :descriptors_catalog, :descriptors_catalog_1
end
# Descriptor selection 4.4
report.value "descriptors_selection", (model.class == OpenTox::Model::LazarRegression ? "Correlation with dependent variable (Pearson p <= 0.05)" : "none")
# Algorithm and descriptor generation 4.5
report.value "descriptors_generation", "lazar"
# Software name and version for descriptor generation 4.6
report.change_catalog :software_catalog, :software_catalog_2, {:name => "lazar, submitted version: #{lazar_commit}", :description => "", :number => "2", :url => "", :contact => ""}
report.ref_catalog :descriptors_generation_software, :software_catalog, :software_catalog_2
# Chemicals/Descriptors ratio 4.7
report.value "descriptors_chemicals_ratio", (model.class == OpenTox::Model::LazarRegression ? "variable (local regression models)" : "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>
No predictions are made for query compounds without similar structures
in the training data. Similarity is determined as the Tanimoto coefficient of
Molprint 2D fingerprints with a threshold of 0.1.
</p>
<p>
Predictions based on a low number and/or very dissimilar neighbors or
on neighbors with conflicting experimental measurements
should be treated with caution.
</p>
</body>
</html>"
# Method used to assess the applicability domain 5.2
report.value "app_domain_method", "Number and similarity of training set compounds (part of the main lazar algorithm)"
# 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 => "", :number => "3", :url => "", :contact => ""}
report.ref_catalog :app_domain_software, :software_catalog, :software_catalog_3
# Limits of applicability 5.4
report.value "applicability_limits", "Compounds without similar substances in the training dataset"
# 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 => "on demand"}
# Data for the dependent variable for the training set 6.4
report.change_attributes "dependent_var_availability", {:answer => "Yes"}
# Other information about the training set 6.5
report.value "other_info", "Original data from: #{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
crossvalidations = prediction_model.crossvalidations
block = ""
crossvalidations.each do |cv|
block += "<p>
<p>Num folds: #{cv.folds}</p>
<p>Num instances: #{cv.nr_instances}</p>
<p>Num unpredicted: #{cv.nr_unpredicted}</p>"
if model_type =~ /classification/i
block += "<p>Accuracy: #{cv.accuracy.signif(3)}</p>
<p>Weighted accuracy: #{cv.weighted_accuracy.signif(3)}</p>
<p>True positive rate: #{cv.true_rate[cv.accept_values[0]].signif(3)}</p>
<p>True negative rate: #{cv.true_rate[cv.accept_values[1]].signif(3)}</p>
<p>Positive predictive value: #{cv.predictivity[cv.accept_values[0]].signif(3)}</p>
<p>Negative predictive value: #{cv.predictivity[cv.accept_values[1]].signif(3)}</p>"
end
if model_type =~ /regression/i
block += "<p>RMSE: #{cv.rmse.signif(3)}</p>
<p>MAE: #{cv.mae.signif(3)}</p>
<p>R<sup>2</sup>: #{cv.r_squared.signif(3)}</p>"
end
block += "</p>"
end
report.value "lmo", "<html><head></head><body><b>3 independent 10-fold crossvalidations:</b>"+block+"</body></html>"
end
# Availability of the external validation set 7.1
report.change_attributes "validation_set_availability", {:answer => "No"}
# Available information for the external validation set 7.2
report.change_attributes "validation_set_data", {:cas => "", :chemname => "", :formula => "", :inchi => "", :mol => "", :smiles => ""}
# Data for each descriptor variable for the external validation set 7.3
report.change_attributes "validation_set_descriptors", {:answer => "Unknown"}
# Data for the dependent variable for the external validation set 7.4
report.change_attributes "validation_dependent_var_availability", {:answer => "Unknown"}
# 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.
</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 descriptors.
</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>
Please note that lazar predictions are based on neighbors.
Descriptors are only used for the calculation of similarities.
</p>
</body>
</html>"
# Comments 9.1
report.value "comments", "<html><head></head><body>
<p>
Public model interface: https://lazar.in-silico.ch
</p>
<p>
Source code: #{lazar_commit}
</p>
<p>
Docker image: https://hub.docker.com/r/insilicotox/lazar/
</p>
</body>
</html>"
# Bibliography 9.2
report.change_catalog :publications_catalog, :publications_catalog_1, {:title => "Helma (2017), Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties", :url => "https://dx.doi.org/10.3389%2Ffphar.2017.00377"}
report.change_catalog :publications_catalog, :publications_catalog_2, {:title => "Lo Piparo (2014), Automated and reproducible read-across like models for predicting carcinogenic potency", :url => "https://doi.org/10.1016/j.yrtph.2014.07.010"}
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.change_catalog :publications_catalog, :publications_catalog_4, {:title => "Bender et al. (2004), Molecular similarity searching using atom environments, information-based feature selection, and a nave bayesian classifier.", :url => "https://doi.org/10.1021/ci034207y"}
report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_1
report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_2
report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_3
report.ref_catalog :bibliography, :publications_catalog, :publications_catalog_4
# output
t = Tempfile.new
t << report.to_xml
name = prediction_model.species.sub(/\s/,"-")+"-"+prediction_model.endpoint.downcase.sub(/\s/,"-")
send_file t.path, :filename => "QMRF_report_#{name.gsub!(/[^0-9A-Za-z]/, '_')}.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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