{"id":"W1975392270","doi":"10.1016/j.molimm.2008.10.013","title":"Deciphering epitope specificities within polyserum using affinity selection of random peptides and a novel algorithm based on pattern recognition theory","year":2008,"lang":"en","type":"article","venue":"Molecular Immunology","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Epitope; Monoclonal antibody; Linear epitope; Epitope mapping; Computational biology; Antibody; Selection (genetic algorithm); Biology; Virology; Computer science; Artificial intelligence; Immunology","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002598338,0.0001563692,0.0003345264,0.0001951236,0.0001400351,0.000007433984,0.00005681036,0.0001149591,0.00009540463],"category_scores_gemma":[0.0001154643,0.0001368073,0.00009348247,0.0001492938,0.000319598,0.00003808142,0.00004421386,0.0002786712,0.000006380383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004125529,"about_ca_system_score_gemma":0.00008964205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003135763,"about_ca_topic_score_gemma":0.000008027187,"domain_scores_codex":[0.9988692,0.0001800385,0.0002716084,0.0002320499,0.000207288,0.000239788],"domain_scores_gemma":[0.9994301,0.0001815946,0.00009728689,0.0001385993,0.000101989,0.00005039155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002926328,0.000298137,0.003066701,0.00006428893,0.0002132287,0.0001653772,0.0003074578,0.0001112189,0.9126425,0.00005505887,0.000004224633,0.08014546],"study_design_scores_gemma":[0.005783084,0.001736027,0.03137971,0.0003191156,0.00009498092,0.001218528,0.0003363281,0.02836031,0.9299017,0.0004923938,0.00009445436,0.0002833308],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9258059,0.0004954376,0.07296032,0.0001359613,0.00008726164,0.0002126984,0.00002081723,0.00002394387,0.0002576452],"genre_scores_gemma":[0.9901137,0.0001415407,0.009333398,0.0002197123,0.00005529889,0.000009571054,0.00002556328,0.00002302286,0.00007821221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07986213,"threshold_uncertainty_score":0.5578839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04476201797980432,"score_gpt":0.2804184651118629,"score_spread":0.2356564471320586,"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."}}