{"id":"W2102803266","doi":"10.1093/bioinformatics/19.2.234","title":"A naive Bayes model to predict coupling between seventransmembrane domain receptors and G-proteins","year":2003,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"AstraZeneca (Canada)","funders":"","keywords":"G protein-coupled receptor; Computational biology; Protein sequencing; Bayesian probability; Transmembrane domain; Bayes' theorem; Computer science; Coupling (piping); Biology; Artificial intelligence; Bioinformatics; Machine learning; Peptide sequence; Genetics; Receptor; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004783319,0.001384669,0.002104513,0.001343647,0.0009617602,0.001516067,0.002199076,0.002063346,0.004373425],"category_scores_gemma":[0.007034574,0.0008077479,0.001381157,0.0008549115,0.0008648632,0.001138688,0.000766783,0.001763833,0.001216132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001816111,"about_ca_system_score_gemma":0.001792032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02088693,"about_ca_topic_score_gemma":0.01357836,"domain_scores_codex":[0.9986215,0.0005872672,0.00008395179,0.0003246258,0.0001981875,0.0001844898],"domain_scores_gemma":[0.9937191,0.00493311,0.0003366308,0.00008873266,0.0007327381,0.0001896368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006809263,0.000194284,0.01108652,0.0001640608,0.0001660172,0.0002431307,0.0001549001,0.9168395,0.001050644,0.00840806,0.003285051,0.05772681],"study_design_scores_gemma":[0.00003142745,0.0000381735,0.0004144406,0.00002293425,0.00002018497,0.00002642585,0.00001000541,0.9942257,0.000184745,0.004778822,0.0002355583,0.00001156548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.270357,0.002874217,0.7118545,0.002874797,0.000374502,0.0005159711,0.00209548,0.001301851,0.007751707],"genre_scores_gemma":[0.882888,0.0007767483,0.1040323,0.0009318864,0.0003364749,0.0005696394,0.002349542,0.00008361853,0.008031766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02088693,"threshold_uncertainty_score":0.04153073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009458093025253722,"score_gpt":0.2407460157306361,"score_spread":0.2312879227053823,"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."}}