{"id":"W2981162329","doi":"10.1016/j.compbiolchem.2019.107145","title":"PeSA: A software tool for peptide specificity analysis","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motif (music); Computational biology; Peptide; Substrate specificity; Computer science; Protein–protein interaction; Bioinformatics; Biology; Biochemistry; Enzyme","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008470457,0.0001174626,0.0001994808,0.00001885407,0.00006172308,0.00001368908,0.00009749975,0.0001632078,0.0002033835],"category_scores_gemma":[0.00007975598,0.0001082048,0.0002098328,0.00009626871,0.0000738928,0.000001883242,0.00006012126,0.00005046582,0.000006982186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007360132,"about_ca_system_score_gemma":0.00002450486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002019171,"about_ca_topic_score_gemma":8.64575e-7,"domain_scores_codex":[0.999255,0.00001200206,0.0001620105,0.0003809056,0.00004587178,0.0001442316],"domain_scores_gemma":[0.9995799,0.0000870959,0.00006482567,0.000140834,0.00008044227,0.00004691437],"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":[0.000110583,0.00007427881,0.2143153,0.00006747169,0.001052913,4.09596e-7,0.000008429299,0.001550662,0.7795714,0.0001885715,0.001000204,0.002059757],"study_design_scores_gemma":[0.002881143,0.000264067,0.1037388,0.00003190669,0.001482069,0.00003261389,0.000139855,0.01149237,0.7734815,0.01336405,0.09139509,0.001696533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775636,0.0003557472,0.02145321,0.00009582743,0.00001759885,0.00006705526,0.0001094526,0.00001046091,0.0003271122],"genre_scores_gemma":[0.9929827,0.00002729515,0.003968669,0.0001499107,0.000145978,0.00001570337,0.00155253,0.000006847467,0.001150291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1105765,"threshold_uncertainty_score":0.4412463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005345595785304701,"score_gpt":0.241438301268556,"score_spread":0.2360927054832513,"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."}}