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module OpenTox
class Nanoparticle < Substance
include OpenTox
field :core, type: Hash, default: {}
field :coating, type: Array, default: []
field :proteomics, type: Hash, default: {}
attr_accessor :scaled_values
def physchem_neighbors min_sim: 0.9, dataset_id:, prediction_feature_id:, relevant_features:
p name
dataset = Dataset.find(dataset_id)
#relevant_features = {}
measurements = []
substances = []
# TODO: exclude query activities!!!
dataset.substances.each do |s|
if s.core == self.core # exclude nanoparticles with different core
dataset.values(s,prediction_feature_id).each do |act|
measurements << act
substances << s
end
end
end
neighbors = []
substances.each do |substance|
values = dataset.values(substance,prediction_feature_id)
if values
common_descriptors = relevant_features.keys & substance.physchem_descriptors.keys
# scale values
query_descriptors = common_descriptors.collect{|d| (physchem_descriptors[d].median-relevant_features[d]["mean"])/relevant_features[d]["sd"]}
@scaled_values = common_descriptors.collect{|d| [d,(physchem_descriptors[d].median-relevant_features[d]["mean"])/relevant_features[d]["sd"]]}.to_h
neighbor_descriptors = common_descriptors.collect{|d| (substance.physchem_descriptors[d].median-relevant_features[d]["mean"])/relevant_features[d]["sd"]}
neighbor_scaled_values = common_descriptors.collect{|d| [d,(substance.physchem_descriptors[d].median-relevant_features[d]["mean"])/relevant_features[d]["sd"]]}.to_h
#weights = common_descriptors.collect{|d| 1-relevant_features[d]["p_value"]}
weights = common_descriptors.collect{|d| relevant_features[d]["r"]**2}
sim = Algorithm::Similarity.weighted_cosine(query_descriptors,neighbor_descriptors,weights)
neighbors << {
"_id" => substance.id,
"measurements" => values,
"similarity" => sim,
"common_descriptors" => common_descriptors.collect do |id|
{
:id => id,
:scaled_value => neighbor_scaled_values[id],
:p_value => relevant_features[id]["p_value"],
:r_squared => relevant_features[id]["r"]**2}
end
} if sim >= min_sim
end
end
$logger.debug "#{self.name}: #{neighbors.size} neighbors"
neighbors.sort!{|a,b| b["similarity"] <=> a["similarity"]}
neighbors
end
def add_feature feature, value, dataset
unless feature.name == "ATOMIC COMPOSITION" or feature.name == "FUNCTIONAL GROUP" # redundand
case feature.category
when "P-CHEM"
physchem_descriptors[feature.id.to_s] ||= []
physchem_descriptors[feature.id.to_s] << value
physchem_descriptors[feature.id.to_s].uniq!
when "Proteomics"
#proteomics[feature.id.to_s] ||= []
#proteomics[feature.id.to_s] << value
#proteomics[feature.id.to_s].uniq!
physchem_descriptors[feature.id.to_s] ||= []
physchem_descriptors[feature.id.to_s] << value
physchem_descriptors[feature.id.to_s].uniq!
when "TOX"
if feature.name == "Total protein (BCA assay)"
physchem_descriptors[feature.id.to_s] ||= []
physchem_descriptors[feature.id.to_s] << value
physchem_descriptors[feature.id.to_s].uniq!
else
dataset.add self, feature, value
end
dataset.save
dataset_ids << dataset.id
dataset_ids.uniq!
else
warn "Unknown feature type '#{feature.category}'. Value '#{value}' not inserted."
end
end
end
def parse_ambit_value feature, v, dataset
#p dataset
#p feature
# TODO add study id to warnings
v.delete "unit"
# TODO: ppm instead of weights
if v.keys == ["textValue"]
add_feature feature, v["textValue"], dataset
elsif v.keys == ["loValue"]
add_feature feature, v["loValue"], dataset
elsif v.keys.size == 2 and v["errorValue"]
add_feature feature, v["loValue"], dataset
warn "Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
elsif v.keys.size == 2 and v["loQualifier"] == "mean"
add_feature feature, v["loValue"], dataset
warn "'#{feature.name}' is a mean value. Original data is not available."
elsif v.keys.size == 2 and v["loQualifier"] #== ">="
warn "Only min value available for '#{feature.name}', entry ignored"
elsif v.keys.size == 2 and v["upQualifier"] #== ">="
warn "Only max value available for '#{feature.name}', entry ignored"
elsif v.keys.size == 3 and v["loValue"] and v["loQualifier"].nil? and v["upQualifier"].nil?
add_feature feature, v["loValue"], dataset
warn "loQualifier and upQualifier are empty."
elsif v.keys.size == 3 and v["loValue"] and v["loQualifier"] == "" and v["upQualifier"] == ""
add_feature feature, v["loValue"], dataset
warn "loQualifier and upQualifier are empty."
elsif v.keys.size == 4 and v["loValue"] and v["loQualifier"].nil? and v["upQualifier"].nil?
add_feature feature, v["loValue"], dataset
warn "loQualifier and upQualifier are empty."
elsif v.size == 4 and v["loQualifier"] and v["upQualifier"] and v["loValue"] and v["upValue"]
add_feature feature, [v["loValue"],v["upValue"]].mean, dataset
warn "Using mean value of range #{v["loValue"]} - #{v["upValue"]} for '#{feature.name}'. Original data is not available."
elsif v.size == 4 and v["loQualifier"] == "mean" and v["errorValue"]
warn "'#{feature.name}' is a mean value. Original data is not available. Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
add_feature feature, v["loValue"], dataset
elsif v == {} # do nothing
else
warn "Cannot parse Ambit eNanoMapper value '#{v}' for feature '#{feature.name}'."
end
end
end
end
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