{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00209548,0.000798494,0.001013072,0.001795007,0.000642593,0.001377531,0.00203024,0.001332601,0.002197068],"category_scores_gemma":[0.004907091,0.0006431175,0.002394882,0.001949691,0.001125978,0.001024659,0.001747221,0.001908086,0.0009297255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577818,"about_ca_system_score_gemma":0.00124925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01009037,"about_ca_topic_score_gemma":0.01678471,"domain_scores_codex":[0.9988481,0.0004736688,0.00004834029,0.0003150596,0.0001914333,0.0001234009],"domain_scores_gemma":[0.9977136,0.001515668,0.0002216714,0.0002701413,0.0001533026,0.0001256047],"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":[0.0002431736,0.0001364152,0.01208713,0.0001999765,0.0002651617,0.0003799987,0.000509891,0.7565206,0.006620665,0.1217163,0.005037154,0.09628352],"study_design_scores_gemma":[0.000005579913,0.00001180421,0.0005549222,0.000007943947,0.00001766561,0.00004752384,0.00001581926,0.954142,0.000294739,0.04397737,0.0009122716,0.00001235606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01165459,0.0004240475,0.9853532,0.0003849979,0.0000340562,0.00004069647,0.0006331669,0.0007828266,0.0006924465],"genre_scores_gemma":[0.7059427,0.001048162,0.2808373,0.0008108467,0.0001970537,0.0004666512,0.004066303,0.0003572584,0.00627375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01009037,"threshold_uncertainty_score":0.02006328,"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."}}