{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003421142,0.0001071059,0.0001631067,0.00009265792,0.0001209801,0.00003007415,0.0001132442,0.0001136844,0.000004336053],"category_scores_gemma":[0.000129183,0.00007173943,0.0000809306,0.00007683248,0.0001714526,0.00001767703,0.00005038372,0.0001249825,0.000001386048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001091272,"about_ca_system_score_gemma":0.00009508416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008329919,"about_ca_topic_score_gemma":0.000007440208,"domain_scores_codex":[0.9988325,0.00005106268,0.0007656799,0.00006642843,0.0001412779,0.0001429984],"domain_scores_gemma":[0.998928,0.0001105825,0.0004919851,0.00009720508,0.0003183423,0.00005386554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00243713,0.000351253,0.01182224,0.0003292639,0.0007194647,0.000003229441,0.0005691905,0.495281,0.3241194,0.07425997,0.01383089,0.07627694],"study_design_scores_gemma":[0.007170112,0.01285836,0.06499058,0.0001328679,0.0002007917,0.001384101,0.0005194242,0.3374091,0.02119122,0.02402413,0.5292183,0.000900996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6507185,0.0001320276,0.3474564,0.0009091707,0.0002742412,0.0002201486,0.00008662074,0.000004322928,0.0001986636],"genre_scores_gemma":[0.9407985,0.00004891349,0.05821815,0.0003376959,0.0002712855,0.000006511097,0.0001605875,0.000008727017,0.0001495815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5153874,"threshold_uncertainty_score":0.2925449,"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."}}