{"id":"W4394555010","doi":"10.6084/m9.figshare.19234492","title":"Additional file 18 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":[0.001253555,0.001976797,0.001258384,0.001439038,0.0005513256,0.001565665,0.002200522,0.001942251,0.4581049],"category_scores_gemma":[0.01242995,0.0005719131,0.001339067,0.001860897,0.0003633533,0.0009744504,0.0009677703,0.001419678,0.1047718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00117526,"about_ca_system_score_gemma":0.001407164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008052613,"about_ca_topic_score_gemma":0.01885605,"domain_scores_codex":[0.9994143,0.00008414129,0.00007284614,0.0002062011,0.0001323461,0.0000901511],"domain_scores_gemma":[0.9951639,0.003238223,0.000308391,0.0005402102,0.0005345513,0.0002146315],"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":[0.0002463479,0.00007800895,0.003380609,0.0009267243,0.00007319086,0.00005064037,0.00001526835,0.00092999,0.0001307297,0.0002709921,0.9899778,0.00391965],"study_design_scores_gemma":[0.008180887,0.0004753131,0.03636054,0.001977137,0.0003703829,0.0008513845,0.0002122456,0.009633347,0.002688133,0.01211903,0.9269515,0.0001801024],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001918486,0.00002165872,0.0000817394,0.00006098393,0.00001519005,0.0000122503,0.9991989,0.0002180576,0.0001995032],"genre_scores_gemma":[0.002419836,0.00004236933,0.0006726197,0.0001245726,0.00002393224,0.0001819522,0.9951627,0.0001534932,0.001218474],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4581049,"threshold_uncertainty_score":0.7729475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02654710399049492,"score_gpt":0.2745555960556066,"score_spread":0.2480084920651117,"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."}}