{"id":"W2063046546","doi":"10.1371/journal.pone.0009797","title":"A Live Zebrafish-Based Screening System for Human Nuclear Receptor Ligand and Cofactor Discovery","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Estrogen and related hormone effects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Ontario Institute for Cancer Research; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; Friedrich-Schiller-Universität Jena","keywords":"Zebrafish; Nuclear receptor; Danio; Biology; Cofactor; Cell biology; Transcription factor; Receptor; Gene isoform; Drug discovery; Biochemistry; Computational biology; Gene; Enzyme","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":[0.0007331776,0.001137727,0.0009514255,0.0009181699,0.0007045856,0.0004001044,0.001713224,0.00105528,0.006331456],"category_scores_gemma":[0.0003316623,0.0007013385,0.0007241354,0.0004213169,0.0004901684,0.0004177542,0.0008489859,0.001174156,0.002969073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001086278,"about_ca_system_score_gemma":0.0007117467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004764013,"about_ca_topic_score_gemma":0.01078429,"domain_scores_codex":[0.9993641,0.00008813293,0.00004560174,0.0001821207,0.0002461082,0.00007389269],"domain_scores_gemma":[0.9998079,0.00004348526,0.00003119362,0.00004475508,0.00003128675,0.00004129906],"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":[0.00005945738,0.00002541475,0.0000835122,0.00004408383,0.0000107889,0.00007952332,0.00001865616,0.0001222106,0.9963385,0.0003993343,0.0005430487,0.002275456],"study_design_scores_gemma":[0.00009300394,0.000359639,0.001275963,0.00001484243,0.00006374517,0.0007170029,0.000015501,0.00449828,0.9772923,0.0001827613,0.01543727,0.00004984546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3823062,0.002737575,0.5612099,0.001306995,0.0004502389,0.002810598,0.0188241,0.01164125,0.01871317],"genre_scores_gemma":[0.4429443,0.003086406,0.4959533,0.0005927166,0.00005629912,0.003869851,0.01805773,0.001338014,0.03410144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006331456,"threshold_uncertainty_score":0.02118081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137390158086882,"score_gpt":0.2125058705654224,"score_spread":0.1987668547567342,"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."}}