{"id":"W4306882708","doi":"10.1038/s41597-022-01714-7","title":"The Immune Signatures data resource, a compendium of systems vaccinology datasets","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health; Division of Intramural Research, National Institute of Allergy and Infectious Diseases; U.S. Department of Health and Human Services","keywords":"Compendium; Reverse vaccinology; Data sharing; Systems biology; Immunogenicity; Computer science; Computational biology; Pooling; Data science; Immune system; Bioinformatics; Biology; Medicine; Immunology; Genome; Artificial intelligence; Geography; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002451755,0.0001199697,0.0001572581,0.00005402419,0.0008945659,0.0001947048,0.007145599,0.0000465318,0.00003454933],"category_scores_gemma":[0.0001723823,0.0000880503,0.00002805304,0.0002261449,0.0001686759,0.00002161633,0.01502291,0.0001764183,0.000008064283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007112324,"about_ca_system_score_gemma":0.0001242279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005476376,"about_ca_topic_score_gemma":0.00002388858,"domain_scores_codex":[0.9982572,0.00015278,0.0004272816,0.0005768108,0.000301564,0.0002844024],"domain_scores_gemma":[0.9916615,0.00003863743,0.0002656622,0.007959813,0.00003832726,0.00003599246],"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":[0.00008675808,0.00008474245,0.00005773214,0.00003870588,0.0001136685,0.000001455793,0.0000429002,0.0003415128,0.0491686,0.0006626329,0.9475616,0.001839689],"study_design_scores_gemma":[0.0002875784,0.00006887465,0.0002133637,0.000004208885,0.00002409412,0.00003085225,0.00115135,0.009676687,0.002283895,0.00001566869,0.9861295,0.0001139916],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2127191,0.1725684,0.001822469,0.002470244,0.01454944,0.003422634,0.5865853,0.000102972,0.005759316],"genre_scores_gemma":[0.6463761,0.00009095765,0.000219006,0.0000399875,0.00008553387,0.00001901063,0.3515619,0.00001739247,0.001590054],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.4336571,"threshold_uncertainty_score":0.9982262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03587471517058732,"score_gpt":0.2659537450915435,"score_spread":0.2300790299209562,"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."}}