{"id":"W4394488804","doi":"10.6084/m9.figshare.19234465","title":"Additional file 11 of DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Germline; Phenotype; Germline mutation; Breast cancer; Mutation; Genetics; Cancer; Biology; Computational biology; Oncology; Medicine; Gene","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001341109,0.001889793,0.001216266,0.001439319,0.0005886949,0.001569506,0.002308763,0.001820848,0.4370209],"category_scores_gemma":[0.0133766,0.000544933,0.001365596,0.001831561,0.0003378842,0.0009568047,0.001020905,0.001435879,0.09612355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088705,"about_ca_system_score_gemma":0.001411764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007851174,"about_ca_topic_score_gemma":0.0181524,"domain_scores_codex":[0.9993944,0.00009901379,0.00006981342,0.0002145644,0.0001284486,0.00009378515],"domain_scores_gemma":[0.994779,0.003572981,0.0003342952,0.0005260312,0.0005614401,0.00022638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002387785,0.0000716693,0.003661421,0.0008688024,0.00008305336,0.00004720141,0.00001561122,0.0008757452,0.0001008478,0.0002832311,0.9898376,0.003915926],"study_design_scores_gemma":[0.008746948,0.0004742053,0.04181867,0.002176916,0.0004580248,0.0009680751,0.0002376648,0.01068026,0.002357749,0.01403359,0.917852,0.0001959401],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002085648,0.00002317997,0.00009383763,0.00006279817,0.00001237933,0.00001386798,0.9991824,0.0001999787,0.0002030069],"genre_scores_gemma":[0.002713825,0.00004451847,0.0007112296,0.0001423066,0.00002442463,0.000239417,0.9947035,0.0001507029,0.001270082],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4370209,"threshold_uncertainty_score":0.8030213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247310446754038,"score_gpt":0.2667114300747402,"score_spread":0.2442383256071998,"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."}}