{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006732603,0.001337723,0.001553158,0.004584924,0.0008344897,0.002481535,0.003314155,0.001606189,0.03172624],"category_scores_gemma":[0.0237697,0.001023534,0.001939694,0.006155911,0.0005174367,0.001417066,0.003530975,0.002634228,0.01908964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134619,"about_ca_system_score_gemma":0.005706562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00742569,"about_ca_topic_score_gemma":0.01172135,"domain_scores_codex":[0.9964276,0.0008840432,0.0008517212,0.0007215092,0.0008369538,0.0002782418],"domain_scores_gemma":[0.989338,0.005003398,0.001491467,0.001960461,0.001417974,0.0007886437],"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":[0.0008109445,0.0001285202,0.01466949,0.006428977,0.0007381185,0.0003017932,0.0002696669,0.002962865,0.004700032,0.00909705,0.9243415,0.03555101],"study_design_scores_gemma":[0.000709228,0.0001128315,0.01577165,0.0009583851,0.0002901265,0.000266808,0.0001437505,0.001480448,0.003017076,0.009670964,0.9674821,0.00009661874],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004719018,0.0001724619,0.002264223,0.000131481,0.00003919222,0.0001078732,0.9950964,0.0008802781,0.0008362617],"genre_scores_gemma":[0.001587821,0.000157861,0.005248576,0.0001566465,0.00001274829,0.0005744013,0.9916787,0.0002407458,0.0003424697],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03172624,"threshold_uncertainty_score":0.1061348,"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."}}