{"id":"W4404579236","doi":"10.1101/2024.11.19.624425","title":"Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA0201","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Peptide; Amino acid; Chemistry; Non canonical; Computational biology; Computer science; Artificial intelligence; Biochemistry; Biology; Cell biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001311229,0.0006822674,0.0005791202,0.000697032,0.0002609193,0.0004825058,0.000578383,0.0009303617,0.001047471],"category_scores_gemma":[0.001817676,0.0002211427,0.0006061282,0.0004931993,0.0002333393,0.0002687146,0.0003356748,0.0006168707,0.0003471515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005714421,"about_ca_system_score_gemma":0.0006350057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004172681,"about_ca_topic_score_gemma":0.002524123,"domain_scores_codex":[0.9996725,0.0001239945,0.00002265796,0.00007242779,0.00006710017,0.00004132183],"domain_scores_gemma":[0.9989029,0.0007403935,0.00009592465,0.00003602815,0.0001888464,0.00003576575],"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":[0.0002055729,0.0004197751,0.01159911,0.00006613677,0.0001104875,0.00009066745,0.00002492188,0.9102536,0.01018619,0.0003517826,0.0005440871,0.06614763],"study_design_scores_gemma":[0.000002818691,0.00003395407,0.0005903125,0.000001883122,0.000004342292,0.000006287697,0.000001809001,0.9981457,0.00109951,0.00006661128,0.00004447936,0.000002319454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.786652,0.000920712,0.2079439,0.0003993039,0.00009157996,0.00009693161,0.0002826468,0.001397437,0.002215561],"genre_scores_gemma":[0.968662,0.0001019275,0.02998869,0.00006605573,0.00001566699,0.00005298654,0.0001937095,0.00001430817,0.0009045628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004172681,"threshold_uncertainty_score":0.008296788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150247864739919,"score_gpt":0.2249063888535777,"score_spread":0.2134039102061785,"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."}}