{"id":"W4282976454","doi":"10.1186/s12885-022-09747-2","title":"KAZN as a diagnostic marker in ovarian cancer: a comprehensive analysis based on microarray, mRNA-sequencing, and methylation data","year":2022,"lang":"en","type":"article","venue":"BMC Cancer","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Surgical oncology; Ovarian cancer; Methylation; DNA methylation; Medicine; Microarray; Computational biology; Microarray analysis techniques; Oncology; Bioinformatics; Cancer; Biology; Internal medicine; Gene expression; Gene; Genetics","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":[0.0006397201,0.0003108517,0.0004364001,0.0011274,0.0003621964,0.0003779774,0.0001708796,0.0001856597,0.0003624078],"category_scores_gemma":[0.0004491688,0.0001354815,0.0003529894,0.001009536,0.0001925713,0.0002131128,0.0003098264,0.0002305705,0.0001369328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003214528,"about_ca_system_score_gemma":0.0004330426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001133909,"about_ca_topic_score_gemma":0.002725111,"domain_scores_codex":[0.9996562,0.0000548291,0.00003117499,0.0001064148,0.0001174164,0.00003401201],"domain_scores_gemma":[0.9997943,0.00004479034,0.00006132011,0.00001681662,0.0000558096,0.00002703021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005125732,0.00008897978,0.168248,0.000546838,0.0002735618,0.0002968624,0.000192827,0.0013595,0.7669046,0.0002276762,0.0009137949,0.0604348],"study_design_scores_gemma":[0.00003128012,0.0005245816,0.7394674,0.00005318741,0.0008758855,0.002219009,0.0002969171,0.01928843,0.2227489,0.0008085454,0.0136096,0.00007629106],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9624605,0.006354341,0.02483193,0.0002326356,0.00002866463,0.00009140608,0.004537134,0.0002238718,0.00123942],"genre_scores_gemma":[0.9497088,0.002560696,0.03979079,0.0001314958,0.00004677954,0.0001150858,0.006770356,0.00003183663,0.0008442466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001133909,"threshold_uncertainty_score":0.003383219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341500783650765,"score_gpt":0.2849191251280028,"score_spread":0.2615041172914952,"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."}}