{"id":"W4385633228","doi":"10.1101/2023.08.04.551871","title":"PoxiPred: An artificial intelligence-based method for the prediction of potential antigens and epitopes to accelerate vaccine development efforts against poxviruses","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Poxvirus research and outbreaks","field":"Immunology and Microbiology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Research Nova Scotia; Canadian Institutes of Health Research; Dalhousie University; Genome Canada; Dalhousie Medical Research Foundation","keywords":"Epitope; Antigen; Virology; Biology; Computational biology; Epitope mapping; Proteome; Immunology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001140285,0.001447067,0.0006060827,0.00131474,0.0003986556,0.001096621,0.001185218,0.001153319,0.007853399],"category_scores_gemma":[0.003019468,0.0004568513,0.0009627375,0.0005379166,0.0003088632,0.0009909656,0.0008455132,0.001063263,0.002124623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004256192,"about_ca_system_score_gemma":0.0008117607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001497812,"about_ca_topic_score_gemma":0.001635793,"domain_scores_codex":[0.9996635,0.00008470903,0.00002587069,0.0001079389,0.00008275636,0.00003533225],"domain_scores_gemma":[0.9990537,0.0006163607,0.00007345639,0.00007393683,0.0001243622,0.00005820862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001815151,0.0007762737,0.02491022,0.001358703,0.0006374687,0.001022302,0.000281243,0.2431606,0.042726,0.007796774,0.1053763,0.570139],"study_design_scores_gemma":[0.00009428328,0.0001117936,0.001342821,0.00004198584,0.00004231736,0.0001299326,0.00002567351,0.9769183,0.008462024,0.004681169,0.008128781,0.00002088044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07773878,0.001085989,0.8004981,0.0008908438,0.0003478292,0.0004439781,0.006207688,0.1071484,0.00563828],"genre_scores_gemma":[0.215555,0.0004396059,0.7652737,0.0007875531,0.0001092891,0.0005707098,0.01088826,0.002193108,0.004182809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007853399,"threshold_uncertainty_score":0.02627218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05338545209942569,"score_gpt":0.2932715670514778,"score_spread":0.2398861149520521,"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."}}