{"id":"W4324117491","doi":"10.1016/j.gimo.2023.100634","title":"P587: Leveraging extensive datasets to better classify SMARCA4 variants*","year":2023,"lang":"en","type":"article","venue":"Genetics in Medicine Open","topic":"Chromatin Remodeling and Cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"","keywords":"SMARCA4; Computer science; Computational biology; Data science; Biology; Genetics; Gene","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":[0.0005645989,0.0001653162,0.000230947,0.000101414,0.00006785543,0.00003479325,0.0006649502,0.0001029359,0.0001045671],"category_scores_gemma":[0.0001933785,0.0001494411,0.00002528394,0.0003089583,0.00005960675,0.000003239666,0.0008183574,0.000129476,0.00009559262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002566624,"about_ca_system_score_gemma":0.00008896425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001716315,"about_ca_topic_score_gemma":0.0001155835,"domain_scores_codex":[0.9985586,0.00006683086,0.000296567,0.0005195411,0.0002101547,0.0003482451],"domain_scores_gemma":[0.9990709,0.00002352739,0.00005262015,0.0006587206,0.0000644263,0.0001297696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008364436,0.00003149126,0.003508961,0.0000390167,0.00005333097,0.00007750703,0.0005570139,0.002487624,0.4643969,0.00002427327,0.4892993,0.03944097],"study_design_scores_gemma":[0.006128131,0.001103255,0.08862223,0.0007691621,0.00009252583,0.00008267256,0.001887175,0.009234049,0.07360618,0.002047902,0.8151412,0.001285527],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859877,0.0005928712,0.002229469,0.006171507,0.0006318521,0.0005914791,0.00009600096,0.00002157641,0.003677598],"genre_scores_gemma":[0.9735871,0.0009126394,0.00672471,0.0136838,0.001123806,0.0001055289,0.001745368,0.00007054595,0.002046529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3907907,"threshold_uncertainty_score":0.6094033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0576557045035023,"score_gpt":0.3654791469859009,"score_spread":0.3078234424823986,"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."}}