{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003088783,0.002483069,0.00215791,0.005453745,0.0009851285,0.002322217,0.001896969,0.002636964,0.005037189],"category_scores_gemma":[0.01102642,0.0004495477,0.002587187,0.003132024,0.000423408,0.001580935,0.00217456,0.001952774,0.006683991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005841085,"about_ca_system_score_gemma":0.001041458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006438084,"about_ca_topic_score_gemma":0.01137569,"domain_scores_codex":[0.9974692,0.000529101,0.0002596224,0.001152418,0.000392585,0.000197093],"domain_scores_gemma":[0.9965848,0.001776314,0.0002583009,0.0005653412,0.0005108144,0.0003044008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004551126,0.001176465,0.456038,0.002993101,0.006237118,0.003117424,0.0002879451,0.02539114,0.0179783,0.001474001,0.2373767,0.2433785],"study_design_scores_gemma":[0.002102946,0.002904313,0.2715276,0.001333049,0.005294672,0.009050387,0.001715394,0.3696157,0.01930914,0.02437568,0.2922903,0.0004807849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.461971,0.02126283,0.03841917,0.0056409,0.001774802,0.0006458974,0.4466437,0.01331578,0.01032593],"genre_scores_gemma":[0.3193706,0.002612333,0.03091155,0.001197124,0.0005278472,0.0004571222,0.6416782,0.000846678,0.002398581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006438084,"threshold_uncertainty_score":0.01685107,"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."}}