{"id":"W2113979528","doi":"10.1109/fbit.2007.21","title":"Classification of Cell Membrane Proteins","year":2007,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pseudo amino acid composition; Jackknife resampling; Transmembrane protein; Membrane protein; In silico; Computer science; Protein sequencing; Feature (linguistics); Representation (politics); Cell membrane; Artificial intelligence; Computational biology; Function (biology); Pattern recognition (psychology); Biological system; Membrane; Peptide sequence; Amino acid; Chemistry; Biochemistry; Biology; Mathematics; Cell biology; Receptor; Gene","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":[0.0004586387,0.0005150614,0.0005733239,0.002821994,0.000433486,0.0009851026,0.0006517227,0.001057158,0.001606445],"category_scores_gemma":[0.001687102,0.00008483951,0.0005605732,0.001554889,0.0003007161,0.0006410066,0.0004933559,0.0004372329,0.001331562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004323639,"about_ca_system_score_gemma":0.0004667918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001562153,"about_ca_topic_score_gemma":0.001048192,"domain_scores_codex":[0.9994525,0.0000545696,0.0000473867,0.0001020006,0.000253766,0.00008970318],"domain_scores_gemma":[0.999012,0.0002153953,0.0001439134,0.0001046039,0.0004064876,0.0001176767],"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":[0.001055608,0.0004686409,0.1172076,0.0008084583,0.0001563709,0.001189411,0.0002011794,0.01905507,0.2537619,0.005771471,0.01766388,0.5826604],"study_design_scores_gemma":[0.00009601312,0.000491332,0.1903225,0.000164272,0.0001514134,0.003168171,0.0006539654,0.5671116,0.1658733,0.01210668,0.05976957,0.00009129679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.820659,0.004466013,0.1530277,0.0007739638,0.0004489814,0.000430836,0.008433794,0.002217632,0.009542024],"genre_scores_gemma":[0.8776385,0.001325928,0.1014817,0.0002335027,0.0001416609,0.0001830183,0.01514702,0.00007473066,0.003773898],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002821994,"threshold_uncertainty_score":0.005374074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008142779209924384,"score_gpt":0.2540919090952387,"score_spread":0.2459491298853143,"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."}}