{"id":"W4313525848","doi":"10.1109/bibm55620.2022.9994928","title":"Hierarchical Categorical Generative Modeling for Multi-omics Cancer Subtyping","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Bioscience Database Center; Ministry of Education","keywords":"Subtyping; Overfitting; Categorical variable; Computer science; Machine learning; Generative grammar; Generative model; Artificial intelligence; Cancer; Data mining; Biology; Artificial neural network","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":[0.0003677204,0.0002207847,0.0002197744,0.000179951,0.0003319246,0.0000704941,0.0003450801,0.0001051456,0.00008874887],"category_scores_gemma":[0.00002739016,0.0001924731,0.00008887352,0.0001170657,0.0001020704,0.00001415924,0.0002340803,0.0002894851,0.000002440254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007437357,"about_ca_system_score_gemma":0.0001563577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003285521,"about_ca_topic_score_gemma":0.00002102185,"domain_scores_codex":[0.9986006,0.00002471541,0.0005094934,0.0002568228,0.000315722,0.0002926294],"domain_scores_gemma":[0.9993172,0.00002003078,0.0001858114,0.0001800528,0.0001628694,0.0001340564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005653187,0.001763873,0.001447228,0.0007590853,0.003107378,0.00003493976,0.01066233,0.08759116,0.2642947,0.2000897,0.1011607,0.3234357],"study_design_scores_gemma":[0.001390187,0.0005058072,0.00001625696,0.00001587849,0.00002149108,0.00002680989,0.0009410495,0.98116,0.0007982572,0.0009069255,0.01395559,0.0002617197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1990553,0.0006855903,0.7822874,0.007811602,0.004281049,0.001320905,0.001662788,0.00004770633,0.00284769],"genre_scores_gemma":[0.9789554,0.00153694,0.01199419,0.003689465,0.0008348604,0.000306819,0.001710789,0.00003017211,0.0009413481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8935689,"threshold_uncertainty_score":0.7848824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07809674224255929,"score_gpt":0.3308368585973386,"score_spread":0.2527401163547793,"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."}}