{"id":"W4283390980","doi":"10.3389/fonc.2022.879607","title":"Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data","year":2022,"lang":"en","type":"article","venue":"Frontiers in Oncology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; Research Institute in Oncology and Hematology; University of Manitoba","funders":"","keywords":"Autoencoder; Differential privacy; Sensitivity (control systems); Computer science; Deep learning; Artificial intelligence; Machine learning; Information sensitivity; Big data; Binary classification; Data mining; Genomics; Biology; Support vector machine; Gene; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.002217493,0.0006314131,0.0006444606,0.0005436031,0.0003031586,0.0008067233,0.001125287,0.0007787985,0.0007077516],"category_scores_gemma":[0.005460096,0.0002984816,0.0007012312,0.0007143656,0.00100233,0.001523215,0.001389586,0.001817558,0.0001714991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001474264,"about_ca_system_score_gemma":0.001201146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003979009,"about_ca_topic_score_gemma":0.004234775,"domain_scores_codex":[0.9989711,0.0003327931,0.00006242416,0.0002482703,0.0002667761,0.0001186977],"domain_scores_gemma":[0.9982401,0.0009834091,0.000209419,0.0003112153,0.0001938807,0.00006191489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003100843,0.000180284,0.008777043,0.0001231213,0.0001441768,0.0002098188,0.0001559012,0.8015652,0.004695829,0.03791581,0.002806165,0.1431165],"study_design_scores_gemma":[0.000005254154,0.00001654238,0.0003399001,0.000005364941,0.000008934855,0.00001565252,0.000006268203,0.9820179,0.001020863,0.01617402,0.0003850773,0.000004255556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05811438,0.0006763864,0.9376929,0.001151024,0.00003839196,0.00004730351,0.0004824493,0.0006045807,0.001192574],"genre_scores_gemma":[0.9151173,0.000670489,0.07978388,0.0005340715,0.00006806827,0.0001269468,0.001035862,0.00003938948,0.002623936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003979009,"threshold_uncertainty_score":0.01172733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04864476356953609,"score_gpt":0.3067025827621194,"score_spread":0.2580578191925833,"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."}}