{"id":"W4307511629","doi":"10.2196/29404","title":"Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods","year":2022,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Antigen; Computer science; Antibody; k-nearest neighbors algorithm; Protein sequencing; Artificial intelligence; Computational biology; Machine learning; Biology; Peptide sequence; Immunology; Gene; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001612875,0.0001142268,0.0002313497,0.0001966872,0.0001172554,0.000005504415,0.0002569919,0.0001552021,0.000008886953],"category_scores_gemma":[0.00005766525,0.0001017851,0.00002447145,0.0001788167,0.0001008582,0.00001906131,0.001092321,0.0001345824,1.268556e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008834787,"about_ca_system_score_gemma":0.00008044806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006709779,"about_ca_topic_score_gemma":0.00000170782,"domain_scores_codex":[0.9988589,0.00006131578,0.0006458885,0.0001366358,0.0001745161,0.0001227577],"domain_scores_gemma":[0.9989789,0.00001209183,0.0005383932,0.0003649781,0.0000851622,0.00002047068],"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":[0.00004445583,0.00007426809,0.003232224,0.0003035811,0.0001483266,3.718988e-8,0.0006210213,0.0000572429,0.7138352,0.00008029408,0.00003334021,0.28157],"study_design_scores_gemma":[0.001323296,0.0009108548,0.004747747,0.00003173978,0.00008876024,0.00006301729,0.002617146,0.4314661,0.5456882,0.00006683871,0.01279583,0.0002004769],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839494,0.00112239,0.01424494,0.00004472001,0.00003751925,0.0003450036,0.0001636564,0.000008682019,0.0000837095],"genre_scores_gemma":[0.9002895,0.0006658194,0.09752586,0.000005696345,0.000004227517,0.00001532673,0.001479354,0.000007974027,0.000006208137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4314089,"threshold_uncertainty_score":0.4150676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0666600225481614,"score_gpt":0.3500390679752069,"score_spread":0.2833790454270455,"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."}}