{"id":"W4387264288","doi":"10.3390/cancers15194801","title":"Genomic Biomarker Discovery in Disease Progression and Therapy Response in Bladder Cancer Utilizing Machine Learning","year":2023,"lang":"en","type":"article","venue":"Cancers","topic":"Bladder and Urothelial Cancer Treatments","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prostate Cancer Canada; University of Alberta; University of Calgary","funders":"National Cancer Institute","keywords":"Biomarker discovery; Bladder cancer; Biomarker; Disease; Computational biology; Cancer; Medicine; Cancer therapy; Bioinformatics; Oncology; Internal medicine; Biology; Genetics; Gene; Proteomics","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":[0.001928655,0.0003394061,0.0006143885,0.001677292,0.0001685512,0.0009790835,0.0002981056,0.0005382429,0.000526504],"category_scores_gemma":[0.004028397,0.0001203693,0.000421358,0.001519329,0.0003383503,0.0005984118,0.0005195063,0.0006955983,0.0001629477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004476437,"about_ca_system_score_gemma":0.0005706917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008754294,"about_ca_topic_score_gemma":0.00120604,"domain_scores_codex":[0.9993508,0.0003675983,0.00003449686,0.00009883939,0.00009810342,0.00005006696],"domain_scores_gemma":[0.9985313,0.0009327451,0.0002898072,0.00009151334,0.0001054178,0.00004902895],"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":[0.00126746,0.0007687896,0.3239627,0.0004772579,0.0005468231,0.0003811225,0.0002381317,0.1770711,0.01945466,0.006068122,0.00230316,0.4674607],"study_design_scores_gemma":[0.00007338812,0.001090948,0.1520393,0.0001728974,0.0003324669,0.0004931375,0.0002915515,0.7913097,0.01948693,0.0298767,0.00473384,0.00009910925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7672124,0.01034131,0.2145295,0.002647492,0.0001349662,0.000162491,0.001464286,0.0007597865,0.002747814],"genre_scores_gemma":[0.972008,0.0008787286,0.0259385,0.0001247839,0.00004583018,0.00004899846,0.0005222224,0.00001404041,0.0004187512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001928655,"threshold_uncertainty_score":0.01019984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03936141403687536,"score_gpt":0.3442427698646037,"score_spread":0.3048813558277283,"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."}}