{"id":"W4309016107","doi":"10.1093/neuonc/noac209.1156","title":"MODL-29. IN SILICO MODELING TO PREDICT ONCOGENICITY AND POTENTIAL TARGETABILITY OF NOVEL FGFR VARIANTS IN PEDIATRIC LOW GRADE GLIOMA","year":2022,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Fibroblast Growth Factor Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Mutant; Fibroblast growth factor receptor 1; Missense mutation; Mutation; In silico; Wild type; Fibroblast growth factor receptor; Cancer research; Gene; Biology; Genetics; Receptor; Fibroblast growth factor","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006594595,0.0001812479,0.0003451441,0.0003213121,0.00006704075,0.000007949026,0.0003680726,0.0001980691,0.00002603859],"category_scores_gemma":[0.0004407792,0.0002080557,0.00007428556,0.0005262468,0.00007432962,0.000008859345,0.001023001,0.000489647,0.000001269046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001532136,"about_ca_system_score_gemma":0.0004492718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002953934,"about_ca_topic_score_gemma":0.0002111222,"domain_scores_codex":[0.9975452,0.0004645062,0.0004908607,0.000704493,0.0003067777,0.0004881218],"domain_scores_gemma":[0.9992532,0.00007872439,0.00009217296,0.0003566359,0.00006381324,0.0001554263],"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":[0.0006784783,0.0007858334,0.03038321,0.00005473085,0.000008072043,0.00005848865,0.0001382951,0.07191823,0.8956991,0.000007765128,0.00004427913,0.0002235489],"study_design_scores_gemma":[0.01509822,0.01078082,0.1374851,0.0000144064,0.00006960265,0.0004929259,0.0004283805,0.4144147,0.418705,0.0003194853,0.001128806,0.001062594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975686,0.0001185368,0.001010184,0.0003143256,0.0002238198,0.0005804384,0.0001170597,0.000009282953,0.00005773949],"genre_scores_gemma":[0.9990364,0.00003552943,0.0005134728,0.0001320473,0.0001158685,0.0000995214,0.00003593135,0.00002809972,0.000003184786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4769941,"threshold_uncertainty_score":0.8484265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01853663400268998,"score_gpt":0.2839253624399358,"score_spread":0.2653887284372458,"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."}}