{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007705751,0.0005444065,0.0005353409,0.0001978969,0.0002102592,0.0003008526,0.0004577031,0.0006210424,0.000005071601],"category_scores_gemma":[0.0001767814,0.00056153,0.0001421746,0.0002464605,0.0000622221,0.00001280012,0.001963127,0.0009901858,0.0000511014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009442319,"about_ca_system_score_gemma":0.0003191626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006190416,"about_ca_topic_score_gemma":0.000004310012,"domain_scores_codex":[0.9976538,0.00007063081,0.0006150317,0.000913176,0.0002348572,0.0005124817],"domain_scores_gemma":[0.9983807,0.00003337496,0.0002904968,0.0008190036,0.0001730949,0.0003032952],"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":[0.00007663717,0.00004470077,0.07259694,0.0007241051,0.0003999785,0.000005582122,0.00004122427,0.0002479375,0.9254757,0.0001308521,0.0001984092,0.00005796449],"study_design_scores_gemma":[0.001845955,0.001225455,0.2842187,0.001167876,0.0007559049,3.647959e-7,0.00008532438,0.01201158,0.6526908,0.00000762612,0.04302208,0.002968375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916728,0.001254043,0.005486756,0.000143295,0.0002397267,0.0007719336,0.0002463233,0.0001283433,0.00005680065],"genre_scores_gemma":[0.9946521,0.000321815,0.003892426,0.00008037275,0.0006482875,0.0002343216,0.00001781018,0.0001267121,0.00002617278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2727849,"threshold_uncertainty_score":0.9996836,"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."}}