{"id":"W2473178036","doi":"10.1007/978-1-61779-276-2_6","title":"Studying Binding Specificities of Peptide Recognition Modules by High-Throughput Phage Display Selections","year":2011,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Computational biology; Phage display; Peptide; Profiling (computer programming); Peptide library; Genome; Biology; Gene; Peptide sequence; Computer science; Genetics; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006405174,0.0005039385,0.0004882661,0.0005178154,0.0002714743,0.0005459478,0.0004409374,0.0003546084,0.0006331245],"category_scores_gemma":[0.0006880735,0.0002857145,0.0002704673,0.0007067126,0.0001896143,0.0002481797,0.0004825065,0.0005314022,0.0003874468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003623526,"about_ca_system_score_gemma":0.0001392283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004265729,"about_ca_topic_score_gemma":0.00076404,"domain_scores_codex":[0.9994414,0.0001325647,0.00003180921,0.00009271166,0.0002127117,0.00008880047],"domain_scores_gemma":[0.9996349,0.0001889091,0.00005085506,0.00003484869,0.00005412285,0.00003637672],"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":[0.00007573408,0.00003913483,0.0007383852,0.00002553834,0.00001175863,0.00004437045,0.00002190278,0.0005745066,0.9957994,0.00006951009,0.000039843,0.002559996],"study_design_scores_gemma":[0.00001059438,0.0001459377,0.00311145,0.00000225839,0.00002249475,0.0001947448,0.00003010392,0.005849696,0.989557,0.0000678755,0.0009978493,0.00001002069],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9662276,0.0006796732,0.03101827,0.00004311225,0.000008797511,0.00009947771,0.0004683548,0.0001617022,0.001293009],"genre_scores_gemma":[0.9691556,0.0008464994,0.0269211,0.0001012359,0.000008795497,0.0001210875,0.001127235,0.0000738719,0.001644653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0006405174,"threshold_uncertainty_score":0.003387392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0561964883321013,"score_gpt":0.3735102408682112,"score_spread":0.3173137525361099,"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."}}