{"id":"W4409586142","doi":"10.21203/rs.3.rs-6255613/v1","title":"A machine learning framework to identify complex physicochemical features of B cell epitopes","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Epitope; Computational biology; Computer science; Chemistry; Artificial intelligence; Biology; Immunology; Antigen","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":[0.0006744589,0.0004959119,0.0005860295,0.0007371584,0.0002990843,0.0008348496,0.0009275422,0.0008261557,0.002085833],"category_scores_gemma":[0.001294939,0.0002640059,0.0007528763,0.0005995121,0.0003957927,0.0007966461,0.0005910407,0.0009967692,0.0005662574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003914156,"about_ca_system_score_gemma":0.0005493805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001266894,"about_ca_topic_score_gemma":0.001334675,"domain_scores_codex":[0.9998434,0.00005241909,0.00000903884,0.00003630477,0.0000428705,0.00001590012],"domain_scores_gemma":[0.9996164,0.0002358563,0.00003422445,0.0000369298,0.00005653851,0.00002012984],"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":[0.000141663,0.0002957405,0.001668536,0.0001713251,0.0001518136,0.0001704593,0.00004525303,0.654193,0.02000201,0.07628269,0.003702735,0.2431748],"study_design_scores_gemma":[0.000005269112,0.00001483396,0.00007857601,0.000002759768,0.000006076961,0.00001304658,0.000002113075,0.9750589,0.0009886903,0.02329452,0.000532214,0.000003036191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008851853,0.0001896451,0.9894956,0.0001795429,0.00001977221,0.00002216969,0.0001062277,0.0003545442,0.0007805372],"genre_scores_gemma":[0.3621261,0.0005875487,0.6310007,0.0002698765,0.0001418535,0.0002025601,0.0006405234,0.0001530159,0.004877776],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002085833,"threshold_uncertainty_score":0.006977797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04415705445527994,"score_gpt":0.3880488878787035,"score_spread":0.3438918334234235,"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."}}