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|
require_relative 'task.rb'
require_relative 'prediction.rb'
require_relative 'batch.rb'
require_relative 'helper.rb'
include OpenTox
configure :production, :development do
$logger = Logger.new(STDOUT)
enable :reloader
also_reload './helper.rb'
also_reload './prediction.rb'
also_reload './batch.rb'
end
before do
@version = File.read("VERSION").chomp
end
not_found do
redirect to('/predict')
end
error do
@error = request.env['sinatra.error']
haml :error
end
get '/?' do
redirect to('/predict')
end
get '/predict/?' do
begin
Process.kill(9,params[:tpid].to_i) if !params[:tpid].blank? #if (Process.getpgid(pid) rescue nil).present?
rescue
nil
end
@existing_datasets = dataset_storage
@models = Model::Validation.all
@models = @models.delete_if{|m| m.model.name =~ /\b(Net cell association)\b/}
endpoints = @models.collect{|m| m.endpoint if m.endpoint != "Mutagenicity"}.compact
endpoints << "Oral toxicity (Cramer rules)"
endpoints << "Lowest observed adverse effect level (LOAEL) (Mazzatorta)"
@endpoints = endpoints.sort.uniq
@models.count <= 0 ? (haml :info) : (haml :predict)
end
get '/task/?' do
if params[:turi]
task = Task.find(params[:turi].to_s)
return JSON.pretty_generate(:percent => task.percent)
elsif params[:predictions]
task = Task.find(params[:predictions])
pageSize = params[:pageSize].to_i - 1
pageNumber= params[:pageNumber].to_i - 1
if params[:model] == "Cramer"
prediction = task.predictions[params[:model]]
compound = Compound.find prediction["compounds"][pageNumber]
image = compound.svg
smiles = compound.smiles
html = "<table class=\"table table-bordered single-batch\"><tr>"
html += "<td>#{image}</br>#{smiles}</br></td>"
string = "<td><table class=\"table\">"
string += "<tr class=\"hide-top\"><td>Cramer rules:</td><td>#{prediction["Cramer rules"][pageNumber.to_i]}</td>"
string += "<tr><td>Cramer rules, with extensions:</td><td>#{prediction["Cramer rules, with extensions"][pageNumber.to_i]}</td>"
string += "</table></td>"
html += "#{string}</tr></table>"
elsif params[:model] == "Mazzatorta"
prediction = task.predictions[params[:model]]
compound = Compound.find prediction["compounds"][pageNumber]
image = compound.svg
smiles = compound.smiles
html = "<table class=\"table table-bordered single-batch\"><tr>"
html += "<td>#{image}</br>#{smiles}</br></td>"
string = "<td><table class=\"table\">"
if prediction["mazzatorta"][pageNumber.to_i][:prediction]
string += "<tr class=\"hide-top\"><td>Prediction:</td><td>#{prediction["mazzatorta"][pageNumber.to_i][:mmol_prediction]} (mmol/kg_bw/day)</td>"
string += "<tr class=\"hide-top\"><td></td><td>#{prediction["mazzatorta"][pageNumber.to_i][:prediction]} (mg/kg_bw/day)</td>"
else
string += "<tr><td>Warnings:</td><td>#{prediction["mazzatorta"][pageNumber.to_i][:warnings]}</td>"
end
string += "</table></td>"
html += "#{string}</tr></table>"
else
predictions = task.predictions[params[:model]].collect{|hash| hash.values[0]}
prediction_object = Prediction.find predictions[pageNumber]
prediction = prediction_object.prediction
compound = Compound.find prediction_object.compound
model = Model::Validation.find prediction_object.model
image = compound.svg
smiles = compound.smiles
type = (model.regression? ? "Regression" : "Classification")
html = "<table class=\"table table-bordered single-batch\"><tr>"
html += "<td>#{image}</br>#{smiles}</br></td>"
string = "<td><table class=\"table\">"
sorter = []
if prediction[:info]
prediction[:info] = "This compound was part of the training dataset. All information from this compound was "\
"removed from the training data before the prediction to obtain unbiased results."
sorter << {"Info" => prediction[:info]}
if prediction["measurements_string"].kind_of?(Array)
sorter << {"Measured activity" => "#{prediction["measurements_string"].join(";")}</br>#{prediction["converted_measurements"].join(";")}"}
else
sorter << {"Measured activity" => "#{prediction["measurements_string"]}</br>#{prediction["converted_measurements"]}"}
end
end
# regression
if prediction[:value] && type == "Regression"
sorter << {"Prediction" => "#{prediction["prediction_value"]}</br>#{prediction["converted_prediction_value"]}"}
sorter << {"95% Prediction interval" => "#{prediction[:interval]}</br>#{prediction["converted_interval"]}"}
sorter << {"Warnings" => prediction[:warnings].join("</br>")}
elsif !prediction[:value] && type == "Regression"
sorter << {"Prediction" => ""}
sorter << {"95% Prediction interval" => ""}
sorter << {"Warnings" => prediction[:warnings].join("</br>")}
# classification
elsif prediction[:value] && type == "Classification"
sorter << {"Consensus prediction" => prediction["Consensus prediction"]}
sorter << {"Consensus confidence" => prediction["Consensus confidence"]}
sorter << {"Structural alerts for mutagenicity" => prediction["Structural alerts for mutagenicity"]}
sorter << {"Lazar mutagenicity (Salmonella typhimurium)" => ""}
sorter << {"Prediction" => prediction[:value]}
sorter << {"Probability" => prediction[:probabilities].collect{|k,v| "#{k}: #{v.signif(3)}"}.join("</br>")}
elsif !prediction[:value] && type == "Classification"
sorter << {"Consensus prediction" => prediction["Consensus prediction"]}
sorter << {"Consensus confidence" => prediction["Consensus confidence"]}
sorter << {"Structural alerts for mutagenicity" => prediction["Structural alerts for mutagenicity"]}
sorter << {"Lazar mutagenicity (Salmonella typhimurium)" => ""}
sorter << {"Prediction" => ""}
sorter << {"Probability" => ""}
#else
sorter << {"Warnings" => prediction[:warnings].join("</br>")}
end
sorter.each_with_index do |hash,idx|
k = hash.keys[0]
v = hash.values[0]
string += (idx == 0 ? "<tr class=\"hide-top\">" : "<tr>")+(k =~ /lazar/i ? "<td colspan=\"2\">" : "<td>")
# keyword
string += "#{k}:"
string += "</td><td>"
# values
string += "#{v}"
string += "</td></tr>"
end
string += "</table></td>"
html += "#{string}</tr></table>"
end
return JSON.pretty_generate(:prediction => [html])
end
end
get '/predict/modeldetails/:model' do
model = Model::Validation.find params[:model]
crossvalidations = Validation::RepeatedCrossValidation.find(model.repeated_crossvalidation_id).crossvalidations
return haml :model_details, :layout=> false, :locals => {:model => model, :crossvalidations => crossvalidations}
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 '/download/dataset/:id' do
response['Content-Type'] = "text/csv"
dataset = Batch.find params[:id]
tempfile = Tempfile.new
tempfile.write(File.read(dataset.source))
tempfile.rewind
send_file tempfile, :filename => dataset.source, :type => (dataset.source =~ /\.smi$/ ? "chemical/x-daylight-smiles" : "text/csv"), :disposition => "attachment"
end
get '/delete/dataset/:id' do
dataset = Batch.find params[:id]
dataset.delete
File.delete File.join(dataset.source)
redirect to("/")
end
get '/predict/csv/:task/:model/:filename/?' do
response['Content-Type'] = "text/csv"
filename = params[:filename]
task = Task.find params[:task].to_s
m = Model::Validation.find params[:model].to_s unless params[:model] =~ /Cramer|Mazzatorta/
dataset = Batch.find_by(:name => filename)
@ids = dataset.ids
warnings = dataset.warnings.blank? ? nil : dataset.warnings.join("\n")
unless warnings.nil?
@parse = []
warnings.split("\n").each do |warning|
if warning =~ /^Cannot/
smi = warning.split("SMILES compound").last.split("at").first
line = warning.split("SMILES compound").last.split("at line").last.split("of").first.strip.to_i
@parse << "Cannot parse SMILES compound#{smi}at line #{line} of #{dataset.source.split("/").last}\n"
end
end
keys_array = []
warnings.split("\n").each do |warning|
if warning =~ /^Duplicate/
text = warning.split("ID").first
numbers = warning.split("ID").last.split("and")
keys_array << numbers.collect{|n| n.strip.to_i}
end
end
@dups = {}
keys_array.each do |keys|
keys.each do |key|
@dups[key] = "Duplicate compound at ID #{keys.join(" and ")}\n"
end
end
end
if params[:model] == "Mazzatorta"
endpoint = "Lowest observed adverse effect level (LOAEL) (Rat) (Mazzatorta)"
elsif params[:model] == "Cramer"
endpoint = "Oral_toxicity_(Cramer_rules)"
else
endpoint = "#{m.endpoint}_(#{m.species})"
end
tempfile = Tempfile.new
if params[:model] =~ /Cramer|Mazzatorta/
# add duplicate and parse warnings
unless warnings.nil?
lines = task.csv.split("\n")
header = lines.shift
out = ""
lines.each_with_index do |line,idx|
if @dups[idx+1]
out << "#{line.tr("\n","")},#{@dups[idx+1]}"
else
out << line+"\n"
end
end
(@parse && !@parse.blank?) ? tempfile.write(header+"\n"+out+"\n"+@parse.join("\n")) : tempfile.write(header+"\n"+out)
#tempfile.write(header+"\n"+out)
else
tempfile.write(task.csv)
end
else
header = task.csv
lines = []
task.predictions[params[:model]].each_with_index do |hash,idx|
identifier = hash.keys[0]
prediction_id = hash.values[0]
# add duplicate warning at the end of a line if ID matches
if @dups && @dups[idx+1]
if prediction_id.is_a? BSON::ObjectId
if @ids.blank?
lines << "#{idx+1},#{identifier},#{Prediction.find(prediction_id).csv.tr("\n","")},#{@dups[idx+1]}"
else
lines << "#{idx+1},#{@ids[idx]},#{identifier},#{Prediction.find(prediction_id).csv.tr("\n","")},#{@dups[idx+1]}"
end
end
else
if prediction_id.is_a? BSON::ObjectId
if @ids.blank?
lines << "#{idx+1},#{identifier},#{Prediction.find(prediction_id).csv}"
else
lines << "#{idx+1},#{@ids[idx]},#{identifier},#{Prediction.find(prediction_id).csv}"
end
else
if @ids.blank?
lines << "#{idx+1},#{identifier},#{p}\n"
else
lines << "#{idx+1},#{@ids[idx]}#{identifier},#{p}\n"
end
end
end
end
(@parse && !@parse.blank?) ? tempfile.write(header+lines.join("")+"\n"+@parse.join("\n")) : tempfile.write(header+lines.join(""))
#tempfile.write(header+lines.join(""))
end
tempfile.rewind
send_file tempfile, :filename => "#{Time.now.strftime("%Y-%m-%d")}_lazar_batch_prediction_#{endpoint}_#{filename}.csv", :type => "text/csv", :disposition => "attachment"
end
post '/predict/?' do
# process batch prediction
if !params[:fileselect].blank? || !params[:existing].blank?
if !params[:existing].blank?
$logger.debug "Take file from database."
@dataset = Batch.find params[:existing].keys[0]
@compounds = @dataset.compounds
@identifiers = @dataset.identifiers
@ids = @dataset.ids
@filename = @dataset.name
end
if !params[:fileselect].blank?
if params[:fileselect][:filename] !~ /\.csv$|\.smi$/
bad_request_error "Wrong file extension for '#{params[:fileselect][:filename]}'. Please upload a .csv or .smi file."
end
@filename = params[:fileselect][:filename].gsub(/\.csv$|\.smi$/,"")
@dataset = Batch.find_by(:name => @filename)
if @dataset
$logger.debug "Take file from database."
@compounds = @dataset.compounds
@identifiers = @dataset.identifiers
@ids = @dataset.ids
else
File.open('tmp/' + params[:fileselect][:filename], "w") do |f|
f.write(params[:fileselect][:tempfile].read)
end
input = Batch.from_csv_file File.join("tmp", params[:fileselect][:filename])
$logger.debug "Processing '#{params[:fileselect][:filename]}'"
if input.class == OpenTox::Batch
@dataset = input
@compounds = @dataset.compounds
@identifiers = @dataset.identifiers
@ids = @dataset.ids
else
File.delete File.join("tmp", params[:fileselect][:filename])
bad_request_error "Could not serialize file '#{@filename}'."
end
end
if @compounds.size == 0
message = @dataset.warnings
@dataset.delete
bad_request_error message
end
end
@models = params[:selection].keys
# for single predictions in batch
@tasks = []
@models.each{|m| t = Task.new; t.save; @tasks << t}
@predictions = {}
task = Task.run do
@models.each_with_index do |model,idx|
t = @tasks[idx]
if model !~ /Cramer|Mazzatorta/
m = Model::Validation.find model
type = (m.regression? ? "Regression" : "Classification")
# add header for regression
if type == "Regression"
unit = (type == "Regression") ? "(#{m.unit})" : ""
converted_unit = (type == "Regression") ? "#{m.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
if @ids.blank?
header = "ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements #{unit},Prediction #{unit},Prediction #{converted_unit},"\
"Prediction Interval Low #{unit},Prediction Interval High #{unit},"\
"Prediction Interval Low #{converted_unit},Prediction Interval High #{converted_unit},"\
"inApplicabilityDomain,Note\n"
else
header = "ID,Original ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements #{unit},Prediction #{unit},Prediction #{converted_unit},"\
"Prediction Interval Low #{unit},Prediction Interval High #{unit},"\
"Prediction Interval Low #{converted_unit},Prediction Interval High #{converted_unit},"\
"inApplicabilityDomain,Note\n"
end
end
# add header for classification
if type == "Classification"
av = m.prediction_feature.accept_values
if @ids.blank?
header = "ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements,Consensus Prediction,Consensus Confidence,"\
"Structural alerts for mutagenicity,Lazar Prediction,"\
"Lazar predProbability #{av[0]},Lazar predProbability #{av[1]},inApplicabilityDomain,Note\n"
else
header = "ID,Original ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements,Consensus Prediction,Consensus Confidence,"\
"Structural alerts for mutagenicity,Lazar Prediction,"\
"Lazar predProbability #{av[0]},Lazar predProbability #{av[1]},inApplicabilityDomain,Note\n"
end
end
# predict compounds
p = 100.0/@compounds.size
counter = 1
predictions = []
@compounds.each_with_index do |cid,idx|
compound = Compound.find cid
if Prediction.where(compound: compound.id, model: m.id).exists?
prediction_object = Prediction.find_by(compound: compound.id, model: m.id)
prediction = prediction_object.prediction
prediction_id = prediction_object.id
# in case prediction object was created by single prediction
if prediction_object.csv.blank?
prediction_object[:csv] = prediction_to_csv(m,compound,prediction)
prediction_object.save
end
# identifier
identifier = @identifiers[idx]
else
prediction = m.predict(compound)
# save prediction object
prediction_object = Prediction.new
prediction_id = prediction_object.id
prediction_object[:compound] = compound.id
prediction_object[:model] = m.id
if type == "Classification"# consensus mutagenicity
sa_prediction = KaziusAlerts.predict(compound.smiles)
lazar_mutagenicity = prediction
confidence = 0
lazar_mutagenicity_val = (lazar_mutagenicity[:value] == "non-mutagenic" ? false : true)
if sa_prediction[:prediction] == false && lazar_mutagenicity_val == false
confidence = 0.85
elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == true
confidence = 0.85 * ( 1 - sa_prediction[:error_product] )
elsif sa_prediction[:prediction] == false && lazar_mutagenicity_val == true
confidence = 0.11
elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == false
confidence = ( 1 - sa_prediction[:error_product] ) - 0.57
end
prediction["Consensus prediction"] = sa_prediction[:prediction] == false ? "non-mutagenic" : "mutagenic"
prediction["Consensus confidence"] = confidence.signif(3)
prediction["Structural alerts for mutagenicity"] = sa_prediction[:matches].blank? ? "none" : sa_prediction[:matches].collect{|a| a.first}.join("; ").gsub(/,/," ")
end
# add additionally fields for html representation
unless prediction[:value].blank? || type == "Classification"
prediction[:prediction_value] = "#{prediction[:value].delog10.signif(3)} #{unit}"
prediction["converted_prediction_value"] = "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)} #{converted_unit}"
end
unless prediction[:prediction_interval].blank?
interval = prediction[:prediction_interval]
prediction[:interval] = "#{interval[1].delog10.signif(3)} - #{interval[0].delog10.signif(3)} #{unit}"
prediction[:converted_interval] = "#{compound.mmol_to_mg(interval[1].delog10).signif(3)} - #{compound.mmol_to_mg(interval[0].delog10).signif(3)} #{converted_unit}"
end
prediction["unit"] = unit
prediction["converted_unit"] = converted_unit
if prediction[:measurements].is_a?(Array)
prediction["measurements_string"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} #{unit}"} : prediction[:measurements].join("</br>")
prediction["converted_measurements"] = prediction[:measurements].collect{|value| "#{compound.mmol_to_mg(value.delog10).signif(3)} #{unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"} if type == "Regression"
else
output["measurements_string"] = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} #{unit}}" : prediction[:measurements]
output["converted_measurements"] = "#{compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" if type == "Regression"
end
# store in prediction_object
prediction_object[:prediction] = prediction
prediction_object[:csv] = prediction_to_csv(m,compound,prediction)
prediction_object.save
# identifier
identifier = @identifiers[idx]
end
# collect prediction_object ids with identifier
predictions << {identifier => prediction_id}
t.update_percent((counter*p).ceil > 100 ? 100 : (counter*p).ceil)
counter += 1
end
# write csv
t[:csv] = header
# write predictions
@predictions["#{model}"] = predictions
elsif model == "Mazzatorta"
compounds = @compounds.collect{|cid| c = Compound.find cid; c.smiles}
prediction = LoaelMazzatorta.predict(compounds)
output = {}
output["model_name"] = "Lowest observed adverse effect level (LOAEL) (Rat) (Mazzatorta)"
output["mazzatorta"] = []
#output["mazzatorta"] = prediction
# header
if @ids.blank?
csv = "ID,Input,Endpoint,Unique SMILES,Prediction (mmol/kg_bw/day),Prediction (mg/kg_bw/day),Notes\n"
else
csv = "ID,Original ID,Input,Endpoint,Unique SMILES,Prediction (mmol/kg_bw/day),Prediction (mg/kg_bw/day),Notes\n"
end
# content
compounds.each_with_index do |smiles, idx|
compound = Compound.find @compounds[idx]
if prediction[idx]["value"]
output["mazzatorta"][idx] = {:mmol_prediction => compound.mg_to_mmol(prediction[idx]["value"].delog10p).signif(3),:prediction => prediction[idx]["value"].delog10p.signif(3)}
else
output["mazzatorta"][idx] = {:warnings => prediction[idx]["warnings"][0].split("\t").first}
end
if @ids.blank?
csv << "#{idx+1},#{@identifiers[idx]},#{output["model_name"]},#{smiles},"\
"#{output["mazzatorta"][idx][:mmol_prediction] if output["mazzatorta"][idx][:mmol_prediction]},"\
"#{output["mazzatorta"][idx][:prediction] if output["mazzatorta"][idx][:prediction]},"\
"#{output["mazzatorta"][idx][:warnings] if output["mazzatorta"][idx][:warnings]}\n"
else
csv << "#{idx+1},#{@ids[idx]},#{@identifiers[idx]},#{output["model_name"]},#{smiles},"\
"#{output["mazzatorta"][idx][:mmol_prediction] if output["mazzatorta"][idx][:mmol_prediction]},"\
"#{output["mazzatorta"][idx][:prediction] if output["mazzatorta"][idx][:prediction]},"\
"#{output["mazzatorta"][idx][:warnings] if output["mazzatorta"][idx][:warnings]}\n"
end
end
predictions = {}
predictions["mazzatorta"] = output["mazzatorta"]
predictions["compounds"] = @compounds
# write csv
t[:csv] = csv
# write predictions
@predictions["#{model}"] = predictions
t.update_percent(100)
else # Cramer model
compounds = @compounds.collect{|cid| c = Compound.find cid; c.smiles}
prediction = [Toxtree.predict(compounds, "Cramer rules"), Toxtree.predict(compounds, "Cramer rules with extensions")]
output = {}
output["model_name"] = "Oral toxicity (Cramer rules)"
output["cramer_rules"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules"]}}.flatten.compact
output["cramer_rules_extensions"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules, with extensions"]}}.flatten.compact
# header
if @ids.blank?
csv = "ID,Input,Endpoint,Unique SMILES,Cramer rules,Cramer rules with extensions\n"
else
csv = "ID,Original ID,Input,Endpoint,Unique SMILES,Cramer rules,Cramer rules with extensions\n"
end
# content
compounds.each_with_index do |smiles, idx|
if @ids.blank?
csv << "#{idx+1},#{@identifiers[idx]},#{output["model_name"]},#{smiles},"\
"#{output["cramer_rules"][idx] != "nil" ? output["cramer_rules"][idx] : "none" },"\
"#{output["cramer_rules_extensions"][idx] != "nil" ? output["cramer_rules_extensions"][idx] : "none"}\n"
else
csv << "#{idx+1},#{@ids[idx]},#{@identifiers[idx]},#{output["model_name"]},#{smiles},"\
"#{output["cramer_rules"][idx] != "nil" ? output["cramer_rules"][idx] : "none" },"\
"#{output["cramer_rules_extensions"][idx] != "nil" ? output["cramer_rules_extensions"][idx] : "none"}\n"
end
end
predictions = {}
predictions["Cramer rules"] = output["cramer_rules"].collect{|rule| rule != "nil" ? rule : "none"}
predictions["Cramer rules, with extensions"] = output["cramer_rules_extensions"].collect{|rule| rule != "nil" ? rule : "none"}
predictions["compounds"] = @compounds
# write csv
t[:csv] = csv
# write predictions
@predictions["#{model}"] = predictions
t.update_percent(100)
end
# save task
# append predictions as last action otherwise they won't save
# mongoid works with shallow copy via #dup
t[:predictions] = @predictions
t.save
end#models
end#main task
@pid = task.pid
#@dataset.delete
#File.delete File.join("tmp", params[:fileselect][:filename])
return haml :batch
end
# single compound prediction
# validate identifier input
if !params[:identifier].blank?
@identifier = params[:identifier].strip
$logger.debug "input:#{@identifier}"
# get compound from SMILES
@compound = Compound.from_smiles @identifier
bad_request_error "'#{@identifier}' is not a valid SMILES string." if @compound.blank?
@models = []
@predictions = []
@toxtree = false
params[:selection].keys.each do |model_id|
if model_id == "Cramer"
@toxtree = true
@predictions << [Toxtree.predict(@compound.smiles, "Cramer rules"), Toxtree.predict(@compound.smiles, "Cramer rules with extensions")]
elsif model_id == "Mazzatorta"
prediction = LoaelMazzatorta.predict(@compound.smiles)
output = {}
if prediction["value"]
output["mazzatorta"] = {:mmol_prediction => @compound.mg_to_mmol(prediction["value"].delog10p).signif(3),:prediction => prediction["value"].delog10p.signif(3)}
else
output["mazzatorta"] = {:warnings => prediction["warnings"][0].split("\t").first}
end
@predictions << output
else
model = Model::Validation.find model_id
@models << model
if Prediction.where(compound: @compound.id, model: model.id).exists?
prediction_object = Prediction.find_by(compound: @compound.id, model: model.id)
prediction = prediction_object.prediction
@predictions << prediction
else
prediction_object = Prediction.new
if model.model.name =~ /kazius/
sa_prediction = KaziusAlerts.predict(@compound.smiles)
prediction = model.predict(@compound)
lazar_mutagenicity = prediction
confidence = 0
lazar_mutagenicity_val = (lazar_mutagenicity[:value] == "non-mutagenic" ? false : true)
if sa_prediction[:prediction] == false && lazar_mutagenicity_val == false
confidence = 0.85
elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == true
confidence = 0.85 * ( 1 - sa_prediction[:error_product] )
elsif sa_prediction[:prediction] == false && lazar_mutagenicity_val == true
confidence = 0.11
elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == false
confidence = ( 1 - sa_prediction[:error_product] ) - 0.57
end
prediction["Consensus prediction"] = sa_prediction[:prediction] == false ? "non-mutagenic" : "mutagenic"
prediction["Consensus confidence"] = confidence.signif(3)
prediction["Structural alerts for mutagenicity"] = sa_prediction[:matches].blank? ? "none" : sa_prediction[:matches].collect{|a| a.first}.join("; ")
else
prediction = model.predict(@compound)
end
prediction_object[:compound] = @compound.id
prediction_object[:model] = model.id
prediction_object[:prediction] = prediction
prediction_object.save
@predictions << prediction
end
end
end
haml :prediction
end
end
get '/help' do
haml :help
end
get '/style.css' do
headers 'Content-Type' => 'text/css; charset=utf-8'
scss :style
end
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