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require 'qsar-report'
require 'rdiscount'
require File.join './npo.rb'
$ambit_search = "http://data.enanomapper.net/substance?type=name&search="
include OpenTox
configure :development do
#$logger = Logger.new(STDOUT)
end
before do
@version = File.read("VERSION").chomp
end
get '/?' do
redirect to('/predict')
end
#=begin
get '/qmrf-report/:id' do
prediction_model = Model::NanoPrediction.find(params[:id])
if prediction_model
model = prediction_model.model
model_type = "regression"
report = QMRFReport.new
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.value "QSAR_title", "Model for #{prediction_model.species} #{prediction_model.endpoint}"
report.change_catalog :software_catalog, :firstsoftware, {:name => "nano-lazar", :description => "nano-lazar toxicity predictions", :number => "1", :url => "https://nano-lazar.in-silico.ch", :contact => "helma@in-silico.ch"}
report.ref_catalog :QSAR_software, :software_catalog, :firstsoftware
report.value "qmrf_date", "#{Time.now.strftime('%d %B %Y')}"
report.change_catalog :authors_catalog, :firstauthor, {:name => "Christoph Helma", :affiliation => "in silico toxicology gmbh", :contact => "Rastatterstrasse 41, CH-4057 Basel, Switzerland", :email => "helma@in-silico.ch", :number => "1", :url => "http://in-silico.ch"}
report.ref_catalog :qmrf_authors, :authors_catalog, :firstauthor
report.change_catalog :authors_catalog, :modelauthor, {:name => "Christoph Helma", :affiliation => "in silico toxicology gmbh", :contact => "Rastatterstrasse 41, CH-4057 Basel, Switzerland", :email => "helma@in-silico.ch", :number => "1", :url => "http://in-silico.ch"}
report.ref_catalog :model_authors, :authors_catalog, :modelauthor
report.value "model_date", "#{Time.parse(model.created_at.to_s).strftime('%Y')}"
report.change_catalog :publications_catalog, :publications_catalog_1, {:title => "Helma, Rautenberg and Gebele (2013), Validation of read across predictions for nanoparticle toxicities ", :url => "in preparation"}
report.ref_catalog :references, :publications_catalog, :publications_catalog_1
report.value "model_species", prediction_model.species
report.change_catalog :endpoints_catalog, :endpoints_catalog_1, {:name => prediction_model.endpoint, :group => ""}
report.ref_catalog :model_endpoint, :endpoints_catalog, :endpoints_catalog_1
report.value "endpoint_units", "#{prediction_model.unit}"
report.value "algorithm_type", "#{model.class.to_s.gsub('Model::Lazar','')}"
#TODO add updated algorithms
#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.neighbor_algorithm.gsub('_',' ').titleize}#{(model.neighbor_algorithm_parameters[:min_sim] ? ' with similarity > ' + model.neighbor_algorithm_parameters[:min_sim].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.prediction_algorithm_parameters
# pred_algorithm_params = (model.prediction_algorithm_parameters[:method] == "rf" ? "random forest" : model.prediction_algorithm_parameters[: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.prediction_algorithm.gsub('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.neighbor_algorithm_parameters[:type]
# report.change_catalog :descriptors_catalog, :descriptors_catalog_1, {:description => "", :name => "#{model.neighbor_algorithm_parameters[:type]}", :publication_ref => "", :units => ""}
# report.ref_catalog :algorithms_descriptors, :descriptors_catalog, :descriptors_catalog_1
#end
# Descriptor selection 4.4
#report.value "descriptors_selection", "#{model.feature_selection_algorithm.gsub('_',' ')} #{model.feature_selection_algorithm_parameters.collect{|k,v| k.to_s + ': ' + v.to_s}.join(', ')}" if model.feature_selection_algorithm
response['Content-Type'] = "application/xml"
return report.to_xml
else
bad_request_error "model with id: #{params[:id]} does not exist."
end
end
#=end
get '/predict/?' do
@prediction_models = []
prediction_models = Model::NanoPrediction.all
prediction_models.each{|m| m.model[:algorithms]["descriptors"]["categories"] == ["P-CHEM"] ? @prediction_models[0] = m : @prediction_models[1] = m}
# define type (pc or pcp)
@prediction_models.each_with_index{|m,idx| idx == 0 ? m[:pc_model] = true : m[:pcp_model] = true}
# collect nanoparticles by training dataset (Ag + Au)
dataset = Dataset.find_by(:name=> "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles")
nanoparticles = dataset.nanoparticles
# select physchem_parameters by relevant features for each model
@pc_relevant_features = @prediction_models[0].model.descriptor_ids.collect{|id, v| Feature.find(id)}
@pcp_relevant_features = @prediction_models[1].model.descriptor_ids.collect{|id, v| Feature.find(id)}
pcp = nanoparticles.sample
pcp.properties.delete_if{|id,v| !@pcp_relevant_features.include?(Feature.find(id))}
@example_pcp = pcp
pc = nanoparticles.sample
pc.properties.delete_if{|id,v| !@pc_relevant_features.include?(Feature.find(id))}
@example_pc = pc
haml :predict
end
get '/license' do
@license = RDiscount.new(File.read("LICENSE.md")).to_html
haml :license, :layout => false
end
post '/predict/?' do
# choose the right prediction model
prediction_model = Model::NanoPrediction.find(params[:prediction_model])
size = params[:size].to_i
@type = params[:type]
example_core = eval(params[:example_core])
example_coating = eval(params[:example_coating])
example_pc = eval(params[:example_pc])
in_core = eval(params[:in_core])
in_core["name"] = params[:input_core]
input_core = in_core
in_coating = eval(params[:in_coating])
in_coating[0]["name"] = params[:input_coating]
input_coating = in_coating
input_pc = {}
(1..size).each{|i| input_pc["#{params["input_key_#{i}"]}"] = [params["input_value_#{i}"].to_f] unless params["input_value_#{i}"] == "-"}
# define relevant_features by input
@type = "pc" ? (@pc_relevant_features = input_pc.collect{|id,v| Feature.find(id)}) : (@pc_relevant_features = [])
@type = "pcp" ? (@pcp_relevant_features = input_pc.collect{|id,v| Feature.find(id)}) : (@pcp_relevant_features = [])
if input_pc == example_pc && input_core == example_core && input_coating == example_coating
# unchanged input = database hit
nanoparticle = Nanoparticle.find_by(:id => params[:example_id])
nanoparticle.properties = input_pc
@match = true
@nanoparticle = nanoparticle
@name = nanoparticle.name
else
# changed input = create nanoparticle to predict
nanoparticle = Nanoparticle.new
nanoparticle.core = input_core
nanoparticle.coating = input_coating
nanoparticle.properties = input_pc
@match = false
@nanoparticle = nanoparticle
end
# prediction output
@input = input_pc
@prediction = prediction_model.model.predict nanoparticle
haml :prediction
end
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