{"id":"W2982686404","doi":"10.7717/peerj.7975","title":"T1000: a reduced gene set prioritized for toxicogenomic studies","year":2019,"lang":"en","type":"article","venue":"PeerJ","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Carleton University; Environment and Climate Change Canada; McGill University","funders":"Genome Prairie; McGill University; Génome Québec; University of Saskatchewan; Environment and Climate Change Canada; Genome Canada; Government of Canada","keywords":"Toxicogenomics; Computational biology; Data mining; Gene regulatory network; Set (abstract data type); Computer science; Gene; Data set; KEGG; Relevance (law); Microarray analysis techniques; Biology; Bioinformatics; Gene expression; Genetics; Artificial intelligence; Gene ontology","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":[0.00112437,0.001295052,0.001552973,0.002084833,0.0005875514,0.001013588,0.0008807292,0.0005992548,0.002683335],"category_scores_gemma":[0.003066611,0.0003590536,0.001974158,0.002184152,0.0004734281,0.0004921258,0.0007903252,0.001007474,0.0006311635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009560648,"about_ca_system_score_gemma":0.00200976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00532505,"about_ca_topic_score_gemma":0.006943368,"domain_scores_codex":[0.999441,0.0001048836,0.00003513182,0.0002009107,0.0001357942,0.00008227717],"domain_scores_gemma":[0.9990892,0.0004733737,0.0001041773,0.00007989526,0.0001752518,0.0000780474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00543799,0.001144842,0.08363124,0.002318637,0.00129191,0.001206822,0.0006270729,0.1337359,0.5454689,0.005111871,0.01626009,0.2037647],"study_design_scores_gemma":[0.001001053,0.00319189,0.1504608,0.0002284231,0.001959565,0.001139613,0.0008476143,0.5724921,0.2148185,0.01381307,0.03969041,0.0003569963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8476169,0.0008997269,0.11867,0.0005604721,0.0001927595,0.0007529077,0.02466976,0.004141279,0.002496243],"genre_scores_gemma":[0.57897,0.000912824,0.3432674,0.0005612192,0.00007555282,0.002612545,0.06850592,0.001113044,0.003981494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00532505,"threshold_uncertainty_score":0.01058811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07507701152722393,"score_gpt":0.3828035732344531,"score_spread":0.3077265617072291,"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."}}