{"id":"W4282925417","doi":"10.1016/j.jbi.2022.104113","title":"Generalized genomic data sharing for differentially private federated learning","year":2022,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"National Center for Advancing Translational Sciences; Natural Sciences and Engineering Research Council of Canada; National Institute on Aging; National Institutes of Health","keywords":"Computer science; Machine learning; Histogram; Mechanism (biology); Artificial intelligence; Competition (biology); Data sharing; Dimension (graph theory); Support vector machine; Federated learning; Data mining; Information privacy; Quality (philosophy); Big data; Image (mathematics); Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00235863,0.0001414774,0.0003266367,0.0003471117,0.0004533555,0.0003934613,0.03920982,0.00007911865,0.00004406111],"category_scores_gemma":[0.008613233,0.0001196357,0.00008144519,0.0004625373,0.00008297502,0.001194172,0.1329266,0.0007988426,0.000003214006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001655814,"about_ca_system_score_gemma":0.0002075037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002764932,"about_ca_topic_score_gemma":3.681358e-7,"domain_scores_codex":[0.9973964,0.00006124998,0.001224292,0.000169679,0.0007856929,0.0003626925],"domain_scores_gemma":[0.995487,0.0001880952,0.001064241,0.003010936,0.0001055586,0.0001441825],"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":[0.0002129921,0.0005403658,0.0008075236,0.0004241462,0.0007778794,0.0001195253,0.001492305,0.00136492,0.01436706,0.00564354,0.6969308,0.2773189],"study_design_scores_gemma":[0.001061442,0.0003085049,0.00007327289,0.00002572814,0.00001875271,0.0001912853,0.0001156179,0.8677345,0.0001944745,0.0159399,0.1141902,0.0001463222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1133549,0.00008596398,0.8788467,0.006486766,0.0008584691,0.0001475521,0.00005208774,0.0001388394,0.0000287356],"genre_scores_gemma":[0.09147318,0.0001087803,0.9074835,0.0005327801,0.0001469124,0.000009434157,0.0002001782,0.00001687432,0.00002842438],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8663695,"threshold_uncertainty_score":0.9997376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06775413814712652,"score_gpt":0.3090394384791483,"score_spread":0.2412853003320218,"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."}}