{"id":"W4394134393","doi":"10.6084/m9.figshare.19234516","title":"Additional file 3 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; Breast cancer; Phenotype; 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":[0.0009946162,0.001767742,0.001287309,0.001488614,0.0006168355,0.001706779,0.002095427,0.001859131,0.4333291],"category_scores_gemma":[0.01064398,0.0005379551,0.001324081,0.002063156,0.0003573583,0.001031043,0.001109254,0.001451615,0.09974336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073053,"about_ca_system_score_gemma":0.001398086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008406957,"about_ca_topic_score_gemma":0.01865866,"domain_scores_codex":[0.9994432,0.00007703574,0.00006423571,0.0002110468,0.0001114919,0.00009290246],"domain_scores_gemma":[0.9959041,0.002771668,0.0002786582,0.0004021226,0.0004324523,0.0002109819],"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":[0.0002203444,0.00005570689,0.003598756,0.001100178,0.00007473098,0.00005757797,0.00001860414,0.0007621432,0.0001354218,0.0003714354,0.9899228,0.003682182],"study_design_scores_gemma":[0.005124881,0.0002743615,0.02559845,0.001718073,0.0003376685,0.0008001867,0.0001787364,0.005966282,0.001932358,0.0126171,0.945308,0.0001440155],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001393908,0.00002175769,0.00007437338,0.00005146326,0.00001024223,0.0000103899,0.9993213,0.0001716959,0.0001994104],"genre_scores_gemma":[0.001996283,0.0000522161,0.0006122986,0.000154188,0.00001944143,0.0001858072,0.9957083,0.0001542139,0.001117241],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4333291,"threshold_uncertainty_score":0.8082873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183733558640098,"score_gpt":0.2653704461002484,"score_spread":0.2435331105138474,"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."}}