{"id":"W2018157754","doi":"10.1016/j.bpj.2010.12.1945","title":"TMBB-DB: A Proteomic Database of Transmembrane β-Barrel Predictions","year":2011,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Center for Research Resources; Natural Sciences and Engineering Research Council of Canada","keywords":"Proteome; Transmembrane protein; Proteomics; Computational biology; Bacterial outer membrane; Signal peptide; Biology; Membrane protein; Bioinformatics; Computer science; Database; Peptide sequence; Membrane; Biochemistry; Escherichia coli","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":[0.0001412068,0.0001204902,0.0001374717,0.00004486346,0.00008221264,0.00001200292,0.0002417262,0.00008495646,0.00008925895],"category_scores_gemma":[0.00005701914,0.00009885209,0.0001409259,0.00007690561,0.0001183651,0.000009618142,0.0000519443,0.0002663483,0.00001838624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000071227,"about_ca_system_score_gemma":0.00007273745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001050544,"about_ca_topic_score_gemma":0.000001147002,"domain_scores_codex":[0.9991798,0.00004934731,0.0003018411,0.0001193127,0.0001658067,0.0001838425],"domain_scores_gemma":[0.9993757,0.00000545208,0.0001594152,0.0002496055,0.00007933396,0.0001304681],"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":[0.0001477982,0.0001976259,0.0005474543,0.00004495772,0.0000650176,0.000003870608,0.0001817328,0.00003615076,0.9973292,0.0001956204,0.0005853658,0.0006652263],"study_design_scores_gemma":[0.001357873,0.00113301,0.008649125,0.00007810478,0.00008500709,0.0003190955,0.0000697619,0.003111347,0.9817369,0.00008674851,0.003095774,0.0002772468],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673699,0.00002806969,0.02953988,0.00006941749,0.0001834247,0.0001587982,0.00005627639,0.000014125,0.002580136],"genre_scores_gemma":[0.9895404,0.00006768325,0.009747458,0.00006250537,0.0003767492,0.0000069796,0.00003946137,0.00001639026,0.0001424003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0221705,"threshold_uncertainty_score":0.4031071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0136588774369851,"score_gpt":0.2458852590153539,"score_spread":0.2322263815783688,"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."}}