{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006870662,0.0009399828,0.0007722898,0.0007698412,0.0003936349,0.000670818,0.0009756404,0.000927153,0.004404518],"category_scores_gemma":[0.0009537122,0.0005497058,0.001090575,0.0003980645,0.000240639,0.0003194587,0.0003945356,0.0006217186,0.0007510454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060713,"about_ca_system_score_gemma":0.001142877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007686167,"about_ca_topic_score_gemma":0.006948233,"domain_scores_codex":[0.9998835,0.00003356339,0.000008592772,0.00002377165,0.00002918703,0.00002139217],"domain_scores_gemma":[0.9995758,0.0002634611,0.00004861063,0.00001841353,0.00005422639,0.00003950603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004369363,0.000182001,0.01249262,0.000339342,0.0001961105,0.0004088496,0.00007485678,0.9656392,0.006033811,0.002254487,0.002972438,0.008969243],"study_design_scores_gemma":[0.00004133799,0.00007212147,0.0006814545,0.000009757871,0.00002346278,0.00004841854,0.00001048213,0.9955917,0.001697811,0.000451219,0.001365581,0.000006728646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8996987,0.001301053,0.06696481,0.0005311807,0.0001328299,0.0003256626,0.01142386,0.008679258,0.01094274],"genre_scores_gemma":[0.9202814,0.0006670419,0.06534985,0.0001639571,0.00002525676,0.0005399302,0.009658244,0.0008133859,0.002500811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007686167,"threshold_uncertainty_score":0.01528287,"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."}}