{"id":"W4383199021","doi":"10.3389/fgene.2023.1184704","title":"Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms","year":2023,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Dali University; Yunnan Provincial Department of Education","keywords":"KEGG; Prostate cancer; Computational biology; Microarray analysis techniques; Biology; Bioinformatics; Machine learning; Cancer; Gene ontology; Gene; Gene expression; Computer science; 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":[0.001111461,0.001061643,0.001499217,0.002859899,0.0003565713,0.0010661,0.0005282928,0.0003751436,0.0006729686],"category_scores_gemma":[0.001083689,0.0001896955,0.001859384,0.002202054,0.0002137061,0.0005023901,0.0003929088,0.0005626225,0.0002331492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005706849,"about_ca_system_score_gemma":0.001150695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308881,"about_ca_topic_score_gemma":0.001053393,"domain_scores_codex":[0.9995589,0.00006858135,0.00003305459,0.0001211281,0.0001540033,0.00006446894],"domain_scores_gemma":[0.9995275,0.0002005982,0.0001011448,0.00003366949,0.0001098764,0.00002729396],"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":[0.0009385672,0.0009056128,0.08567196,0.003120547,0.00171212,0.0005733209,0.000102866,0.2246507,0.08533388,0.004184896,0.006373976,0.5864316],"study_design_scores_gemma":[0.00006063172,0.0007185369,0.04257868,0.0001439817,0.00114876,0.0004050182,0.0000596165,0.9107025,0.0290518,0.005315787,0.009739004,0.00007562771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6113161,0.05795586,0.3123159,0.00091542,0.0001829569,0.0006016659,0.009283185,0.002066385,0.005362493],"genre_scores_gemma":[0.8596719,0.01444905,0.1112618,0.0002178468,0.000109041,0.0004996703,0.01238834,0.00008785329,0.001314525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002859899,"threshold_uncertainty_score":0.005878031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224514711958625,"score_gpt":0.3093047040893795,"score_spread":0.2870595569697932,"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."}}