{"id":"W1971269462","doi":"10.1142/s0219720006001722","title":"THE USE OF FUNCTIONAL DOMAINS TO IMPROVE TRANSMEMBRANE PROTEIN TOPOLOGY PREDICTION","year":2006,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Caprion (Canada); University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Transmembrane protein; Topology (electrical circuits); Transmembrane domain; Membrane protein; Computational biology; Computer science; Biology; Mathematics; Gene; Genetics; Membrane","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":[0.001900736,0.0006224802,0.0004821075,0.001364733,0.0003996074,0.0004454954,0.0007034165,0.0007672003,0.0009267551],"category_scores_gemma":[0.005455315,0.0002890594,0.0005490297,0.000912562,0.0002565249,0.001192486,0.0006042594,0.0004803494,0.0009032122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003075677,"about_ca_system_score_gemma":0.0004260243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001534879,"about_ca_topic_score_gemma":0.001670953,"domain_scores_codex":[0.9994137,0.0002486367,0.00004762625,0.0001304283,0.0001150948,0.00004447844],"domain_scores_gemma":[0.9975581,0.001169671,0.0002100199,0.0003496937,0.0006244067,0.00008810901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009817405,0.0002618805,0.03691197,0.0004658278,0.0001911041,0.0006653473,0.0002932646,0.200441,0.1565825,0.00457065,0.004955121,0.5936797],"study_design_scores_gemma":[0.00002332749,0.0000797107,0.004840493,0.00001672051,0.0000338072,0.0003549678,0.00002141092,0.959756,0.029466,0.003312231,0.002062784,0.0000325682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3121288,0.0006806465,0.6730653,0.000355975,0.00004959181,0.00007315535,0.001176277,0.01033276,0.002137548],"genre_scores_gemma":[0.7200267,0.000238692,0.2774218,0.0001061261,0.00002813435,0.00005023097,0.001349824,0.0002137653,0.0005647725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001900736,"threshold_uncertainty_score":0.01005214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008049881864178819,"score_gpt":0.227180576823814,"score_spread":0.2191306949596352,"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."}}