{"id":"W2105230092","doi":"10.1002/em.21941","title":"Development of a toxicogenomics signature for genotoxicity using a dose‐optimization and informatics strategy in human cells","year":2015,"lang":"en","type":"article","venue":"Environmental and Molecular Mutagenesis","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"National Institute of Environmental Health Sciences; National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism","keywords":"Toxicogenomics; Genotoxicity; Signature (topology); Computational biology; Toxicology; Informatics; Biology; Computer science; Bioinformatics; Genetics; Medicine; Engineering; Toxicity; Gene; Internal medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009360398,0.0006373294,0.0007691742,0.0007241871,0.000315461,0.000763834,0.0004758475,0.0006859311,0.001338183],"category_scores_gemma":[0.0008391344,0.0003330428,0.0008607017,0.0009411272,0.0003852953,0.0003066804,0.0005612717,0.0008097073,0.0006788177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006104436,"about_ca_system_score_gemma":0.0006693728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001614752,"about_ca_topic_score_gemma":0.002820084,"domain_scores_codex":[0.9991154,0.0001462028,0.00007204171,0.0002947643,0.0003189442,0.00005255907],"domain_scores_gemma":[0.9996841,0.00007246664,0.00005651992,0.0000740805,0.00009912859,0.00001375909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001237301,0.00006774493,0.002337744,0.00009690307,0.00002871983,0.00004574532,0.0000463334,0.004028781,0.9810503,0.00017566,0.0001731132,0.01182516],"study_design_scores_gemma":[0.000006969028,0.0002767569,0.007568663,0.000009692235,0.000040083,0.0001168458,0.00005108455,0.008958008,0.9801387,0.0002565,0.002555853,0.00002086998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6252495,0.001617342,0.3577356,0.0003836092,0.00008818362,0.0007447203,0.009432808,0.001593988,0.003154293],"genre_scores_gemma":[0.6932616,0.001982358,0.2874168,0.0003245366,0.00002240969,0.001353707,0.01168327,0.0002705292,0.00368481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001614752,"threshold_uncertainty_score":0.004950345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723225711423558,"score_gpt":0.2628969040009153,"score_spread":0.2356646468866797,"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."}}