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module OpenTox
class Nanoparticle < Substance
include OpenTox
field :core, type: String
field :coating, type: Array, default: []
field :bundles, type: Array, default: []
field :proteomics, type: Hash, default: {}
def nanoparticle_neighbors min_sim: 0.1, type:, dataset_id:, prediction_feature_id:
dataset = Dataset.find(dataset_id)
neighbors = []
p dataset.data_entries.size
p dataset.substance_ids.size
p dataset.substance_ids.collect{|i| i.to_s} == dataset.data_entries.keys
p dataset.substance_ids.collect{|i| i.to_s}
p dataset.data_entries.keys
dataset.nanoparticles.each do |np|
prediction_feature_id
p dataset.data_entries[np.id.to_s]
values = dataset.values(np,prediction_feature_id)
p values
if values
common_descriptors = physchem_descriptors.keys & np.physchem_descriptors.keys
sim = Algorithm::Similarity.cosine(common_descriptors.collect{|d| physchem_descriptors[d]}, common_descriptors.collect{|d| np.physchem_descriptors[d]})
neighbors << {"_id" => np.id, "toxicities" => values, "similarity" => sim} if sim >= min_sim
end
end
neighbors.sort!{|a,b| b["similarity"] <=> a["similarity"]}
neighbors
end
def add_feature feature, value, dataset_id
dataset = Dataset.find(dataset_id)
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!
when "TOX"
# TODO generic way of parsing TOX values
p dataset.name
p self.name
p feature.name
p feature.unit
p value
if feature.name == "7.99 Toxicity (other) ICP-AES" and feature.unit == "mL/ug(Mg)"
dataset.add self, feature, -Math.log10(value)
else
dataset.add self, feature, value
end
dataset.save
else
warn "Unknown feature type '#{feature.category}'. Value '#{value}' not inserted."
end
end
def parse_ambit_value feature, v, dataset_id
dataset = Dataset.find(dataset_id)
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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