{"id":"W4394377619","doi":"10.6084/m9.figshare.19234537","title":"Additional file 8 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":[0.0009007423,0.002212197,0.001208712,0.001368278,0.0005387455,0.001569399,0.002352174,0.002053012,0.4384101],"category_scores_gemma":[0.00848607,0.0006147953,0.001460439,0.001609707,0.0003529172,0.001005978,0.0009675169,0.001584101,0.1154379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110884,"about_ca_system_score_gemma":0.001332879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008359869,"about_ca_topic_score_gemma":0.01915837,"domain_scores_codex":[0.999559,0.00006399098,0.00004502297,0.0001595202,0.00009368741,0.00007879955],"domain_scores_gemma":[0.9972013,0.001907235,0.0001464032,0.0003089903,0.000297955,0.0001381551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001982042,0.00006935534,0.002634333,0.0009827665,0.00007167576,0.00005339678,0.00001389839,0.00133112,0.000154259,0.000343897,0.9900506,0.004096434],"study_design_scores_gemma":[0.006031531,0.0003679884,0.02162059,0.001541639,0.0003022095,0.0007081908,0.0001728058,0.01259713,0.002709401,0.0143458,0.9394535,0.000149287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001996793,0.00002748298,0.0001168497,0.00006928468,0.00001788074,0.00001299204,0.998943,0.0003570033,0.0002558274],"genre_scores_gemma":[0.002597803,0.00005452674,0.0009029349,0.0001386589,0.00002205306,0.0002018626,0.9944764,0.0002051526,0.001400727],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4384101,"threshold_uncertainty_score":0.8010398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02185222859364545,"score_gpt":0.265518858182592,"score_spread":0.2436666295889466,"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."}}