{"id":"W2563115000","doi":"10.1093/toxsci/kfw207","title":"How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology","year":2016,"lang":"en","type":"article","venue":"Toxicological Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Pacific Northwest National Laboratory; Technological University Dublin; Rijksinstituut voor Volksgezondheid en Milieu; U.S. Department of Energy; European Commission; National Institute of Environmental Health Sciences; University of Leeds; Universität des Saarlandes; Liverpool John Moores University; U.S. Army Corps of Engineers; University of Ottawa; Battelle; U.S. Environmental Protection Agency","keywords":"Adverse Outcome Pathway; Computer science; Risk analysis (engineering); Risk assessment; Inference; Outcome (game theory); Regulatory science; Computational model; Skin sensitization; Chemical toxicity; Drug development; Biochemical engineering; Data science; Management science; Computational biology; Artificial intelligence; Biology; Engineering; Medicine; Toxicity; Computer security; Pharmacology; Drug","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002893176,0.0009853279,0.0006737653,0.0009685258,0.0008374909,0.003012966,0.001904154,0.001613314,0.007472991],"category_scores_gemma":[0.01373795,0.0006101828,0.001226326,0.0009140077,0.001345958,0.004394568,0.002661501,0.00312144,0.001110469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306252,"about_ca_system_score_gemma":0.002253066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004235881,"about_ca_topic_score_gemma":0.006037584,"domain_scores_codex":[0.9990723,0.0005449131,0.00005709918,0.0001035148,0.0001704381,0.00005173305],"domain_scores_gemma":[0.995409,0.003361609,0.0002509003,0.0005364824,0.0003025647,0.0001395465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003917941,0.00005604217,0.001436154,0.0001303182,0.00004103066,0.0001077163,0.0001331542,0.5892391,0.0007903921,0.3768672,0.003136393,0.02802334],"study_design_scores_gemma":[0.000009614646,0.00001739945,0.0001258204,0.00003683372,0.00001738653,0.00002741647,0.0000382486,0.6232809,0.0004506831,0.3689119,0.007068208,0.00001571981],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0128177,0.0005140972,0.9587057,0.007938812,0.0001720528,0.0001364718,0.0007999936,0.0007252051,0.01818991],"genre_scores_gemma":[0.388454,0.003025714,0.5990501,0.001290815,0.0001766552,0.0006667131,0.001553744,0.0004067464,0.005375491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007472991,"threshold_uncertainty_score":0.02499968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1684858454864167,"score_gpt":0.3165441525820624,"score_spread":0.1480583070956457,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}