{"id":"W4394428669","doi":"10.6084/m9.figshare.19234483","title":"Additional file 16 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; Breast cancer; Cancer; Germline mutation; Mutation; Oncology; Genetics; Medicine; Computational biology; Biology; Internal 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":[0.001188866,0.001836829,0.001252575,0.001498427,0.0005849202,0.001682024,0.002230455,0.001950172,0.4836968],"category_scores_gemma":[0.01249004,0.0006111122,0.001320658,0.002204424,0.000352613,0.001114069,0.001053317,0.001501998,0.1138435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260524,"about_ca_system_score_gemma":0.001529403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009015605,"about_ca_topic_score_gemma":0.01938697,"domain_scores_codex":[0.9993877,0.00009428251,0.00007514964,0.0002140512,0.000132157,0.0000965992],"domain_scores_gemma":[0.9949297,0.003401865,0.00034136,0.0005084761,0.0005776236,0.0002410391],"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":[0.0001847544,0.00005799893,0.00252865,0.0009061794,0.00005998206,0.00003817236,0.00001420497,0.000708568,0.00009011083,0.0003206983,0.9919021,0.003188618],"study_design_scores_gemma":[0.006376057,0.0002928627,0.02428867,0.001863255,0.0002870041,0.0005803427,0.0001723992,0.006541348,0.001758467,0.01280396,0.9448882,0.0001473685],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000122446,0.00001881493,0.00007364467,0.00005980068,0.00001198136,0.00001151792,0.9993112,0.0001781199,0.0002124281],"genre_scores_gemma":[0.002008413,0.0000489258,0.0006897118,0.0001524142,0.00002343084,0.0002027334,0.9954353,0.0001648267,0.001274312],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4836968,"threshold_uncertainty_score":0.7364439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220374026052638,"score_gpt":0.2667721680537165,"score_spread":0.2447347654484527,"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."}}