{"id":"W2900880986","doi":"10.1016/j.jmb.2018.11.011","title":"Selection of Protein–Protein Interactions of Desired Affinities with a Bandpass Circuit","year":2018,"lang":"en","type":"article","venue":"Journal of Molecular Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute of Mental Health; National Institutes of Health","keywords":"Affinities; Biology; Escherichia coli; Selection (genetic algorithm); Protein–protein interaction; Population; Band-pass filter; Computational biology; Genetics; Gene; Biochemistry; Physics; Computer science; Artificial intelligence","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":[0.0004909858,0.0003950689,0.0004907472,0.0003637166,0.0003247215,0.0008448561,0.0005668341,0.0004332066,0.002536701],"category_scores_gemma":[0.001307974,0.000309502,0.0002474003,0.0003308661,0.0002746664,0.0004856777,0.0005604933,0.000482002,0.0005492823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004304847,"about_ca_system_score_gemma":0.0002986194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000152763,"about_ca_topic_score_gemma":0.0004712474,"domain_scores_codex":[0.9996885,0.00007261265,0.00001790456,0.0000855051,0.00007367444,0.00006183454],"domain_scores_gemma":[0.9994485,0.0002776192,0.00006502299,0.00006179942,0.00005816424,0.0000889445],"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":[0.0002420818,0.00009512264,0.0008499626,0.00003440419,0.00001769077,0.00005574396,0.00001984761,0.003327088,0.9869031,0.002122058,0.000128043,0.006204894],"study_design_scores_gemma":[0.0001408289,0.0003748809,0.002160831,0.000005405482,0.00007316096,0.0001480858,0.00004662441,0.1572878,0.8370296,0.001495636,0.001213137,0.00002405854],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9335052,0.00008153357,0.06357475,0.0001343191,0.00002200652,0.00004789397,0.00005984719,0.0002774467,0.002296986],"genre_scores_gemma":[0.9845962,0.00004974505,0.01433568,0.00006988279,0.00000687521,0.00004572574,0.00004369968,0.00004911465,0.0008030427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002536701,"threshold_uncertainty_score":0.008486152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184091726157907,"score_gpt":0.2498730846501349,"score_spread":0.2380321673885559,"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."}}