{"id":"W4394428927","doi":"10.6084/m9.figshare.19234510","title":"Additional file 22 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":"Breast cancer; Germline; Phenotype; Germline mutation; Genetics; Mutation; Biology; Cancer; Oncology; Computational biology; 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":[0.001037535,0.00171904,0.001311091,0.001534157,0.0006241172,0.001653693,0.002097307,0.00188122,0.4092153],"category_scores_gemma":[0.0100309,0.0005353944,0.001342061,0.002165296,0.0003507448,0.001010188,0.001100546,0.00137253,0.0926349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148066,"about_ca_system_score_gemma":0.001454123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009479085,"about_ca_topic_score_gemma":0.01992313,"domain_scores_codex":[0.9994413,0.00007793244,0.00006994772,0.0001960595,0.0001172199,0.00009753957],"domain_scores_gemma":[0.9960611,0.002473949,0.0003103711,0.000409971,0.0005036542,0.0002408667],"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":[0.0002143059,0.00005852062,0.003083402,0.0009222838,0.00006892061,0.0000518111,0.00001637628,0.0005728426,0.0001285442,0.0003162837,0.9913086,0.003258149],"study_design_scores_gemma":[0.006229328,0.0002768801,0.03115417,0.001902305,0.0003342137,0.0007021966,0.0001872318,0.004834416,0.00190158,0.01078557,0.9415398,0.0001523261],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001518073,0.00002254476,0.00005525798,0.0000593138,0.00001111301,0.000009815853,0.9993293,0.0001551045,0.0002057056],"genre_scores_gemma":[0.001822386,0.0000455464,0.0004673814,0.0001340312,0.00001852949,0.0001523857,0.9962064,0.0001155753,0.00103767],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4092153,"threshold_uncertainty_score":0.8426826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167966230557845,"score_gpt":0.2672292623355403,"score_spread":0.2455496000299619,"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."}}