{"id":"W4394401585","doi":"10.6084/m9.figshare.19234468","title":"Additional file 12 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; Germline mutation; Mutation; Genetics; Cancer; Computational biology; Oncology; 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":[0.001425286,0.00187228,0.00129592,0.001531872,0.0006521477,0.001677702,0.002329925,0.001899816,0.4639299],"category_scores_gemma":[0.01525792,0.0005817597,0.001454804,0.001995691,0.0003614251,0.001038058,0.001030538,0.00152,0.09838337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118724,"about_ca_system_score_gemma":0.001538418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008272146,"about_ca_topic_score_gemma":0.0180672,"domain_scores_codex":[0.9993479,0.0001062589,0.00007888093,0.0002292942,0.0001334004,0.0001042594],"domain_scores_gemma":[0.9938816,0.00426585,0.0003864535,0.0005879745,0.0006077645,0.0002702809],"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":[0.0002645751,0.0000716205,0.003900267,0.0009854956,0.00009200097,0.00005216743,0.00001774342,0.0007934509,0.0001097926,0.0003204355,0.9895242,0.003868293],"study_design_scores_gemma":[0.009860144,0.0004558919,0.04101554,0.002251589,0.0005118881,0.0009739755,0.0002410892,0.009017415,0.002382778,0.01459305,0.9184936,0.0002030515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001778874,0.00002078206,0.00008620277,0.00006277172,0.00001237026,0.00001311373,0.9992635,0.0001779016,0.00018548],"genre_scores_gemma":[0.002671103,0.00004966403,0.0007143212,0.0001646303,0.00002841507,0.0002636244,0.9946178,0.000162186,0.001328214],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4639299,"threshold_uncertainty_score":0.7646389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360000230900877,"score_gpt":0.2661416987139829,"score_spread":0.2425416964049741,"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."}}