{"id":"W4297475121","doi":"10.1002/pro.4442","title":"CSM‐peptides: A computational approach to rapid identification of therapeutic peptides","year":2022,"lang":"en","type":"article","venue":"Protein Science","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; Medical Research Council Canada; Medical Research Council; State Government of Victoria; University of Queensland","keywords":"Identification (biology); Computational biology; Peptide; Chemistry; Combinatorial chemistry; Biochemistry; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001182363,0.00007506561,0.00007388196,0.0001044481,0.0002835853,0.00003752319,0.0005562587,0.00001781161,0.00001646549],"category_scores_gemma":[0.0001804996,0.00007380934,0.00003463083,0.0004629791,0.0002148959,0.00000948009,0.0003111782,0.00008386467,0.000007517508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002574072,"about_ca_system_score_gemma":0.0001626298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007125339,"about_ca_topic_score_gemma":2.448439e-7,"domain_scores_codex":[0.9987955,0.00005767905,0.0002536789,0.0002471391,0.000484235,0.0001617838],"domain_scores_gemma":[0.9993694,0.000008268922,0.0001604508,0.0002836924,0.0001275874,0.00005064436],"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":[0.00002416539,0.00008309275,0.0006200086,0.00003278788,0.000007322436,6.985084e-8,0.0006081634,0.05126841,0.9378583,0.002456234,0.00009895479,0.006942505],"study_design_scores_gemma":[0.000796172,0.001685636,0.02466685,0.00002385675,0.0000189168,0.00006216402,0.002001071,0.2247551,0.7273762,0.002988592,0.01489315,0.0007322881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8875781,0.00008866474,0.1078329,0.0001742467,0.00006309662,0.0006939866,0.00001736259,0.00001756872,0.003534087],"genre_scores_gemma":[0.9707332,9.975759e-7,0.02856335,0.000192306,0.00001939681,0.0001431027,0.00003027397,0.000006616638,0.0003107424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2104821,"threshold_uncertainty_score":0.3009858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237875106079134,"score_gpt":0.2690083737431696,"score_spread":0.2566296226823783,"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."}}