{"id":"W4311914214","doi":"10.1371/journal.pone.0279174","title":"Personalized breast cancer onset prediction from lifestyle and health history information","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Machine Intelligence Institute","keywords":"Breast cancer; Medicine; Cancer; Bioinformatics; Gerontology; Oncology; Computational biology; Internal medicine; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001052709,0.0003491893,0.0005295002,0.000889579,0.0001840906,0.0005374057,0.0006268203,0.0004990256,0.001521811],"category_scores_gemma":[0.002640186,0.0002441844,0.000616821,0.0007598563,0.0002393758,0.0003357374,0.0004043841,0.0008378876,0.0003792665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006899961,"about_ca_system_score_gemma":0.001007424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02129492,"about_ca_topic_score_gemma":0.03137058,"domain_scores_codex":[0.999767,0.00007362916,0.0000109869,0.00008697298,0.00003286791,0.00002856147],"domain_scores_gemma":[0.9992232,0.0004430432,0.0001071561,0.0001045704,0.00007634599,0.00004556602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004869903,0.000425733,0.3719092,0.00009906677,0.0002861817,0.0001504508,0.0002081679,0.3100427,0.003253076,0.004148214,0.004629426,0.3043609],"study_design_scores_gemma":[0.00003469836,0.0001019276,0.05903569,0.00001701899,0.00005563968,0.00009940264,0.00004393551,0.930351,0.0009063312,0.007500516,0.00182865,0.00002512013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.550787,0.0007779881,0.4380944,0.001923041,0.00005376807,0.0001054071,0.004305252,0.001508884,0.002444197],"genre_scores_gemma":[0.9442943,0.000156691,0.05095127,0.0001414198,0.00003089724,0.00004913072,0.002452576,0.00002818564,0.001895647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02129492,"threshold_uncertainty_score":0.04234195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03237320730891078,"score_gpt":0.2356996681844436,"score_spread":0.2033264608755329,"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."}}