{"id":"W2132581719","doi":"10.1002/jcc.21053","title":"Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs","year":2008,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Feature selection; Computer science; Classifier (UML); Benchmark (surveying); Transmembrane protein; Feature (linguistics); Transmembrane domain; Integral membrane protein; Sequence (biology); Artificial intelligence; Pattern recognition (psychology); Membrane protein; Algorithm; Biological system; Chemistry; Amino acid; Membrane; Biology; Biochemistry","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":[0.0005915488,0.0007707547,0.0007310243,0.001812658,0.0003710793,0.0007565319,0.001076844,0.0008160275,0.001043829],"category_scores_gemma":[0.001860793,0.0002702601,0.00117949,0.001036372,0.0002442209,0.0008656502,0.000573266,0.0007150549,0.0007084294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004591993,"about_ca_system_score_gemma":0.0007904334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002514143,"about_ca_topic_score_gemma":0.002672057,"domain_scores_codex":[0.999724,0.00004272861,0.00002179084,0.00008840639,0.00008530703,0.00003770053],"domain_scores_gemma":[0.9991876,0.0003310197,0.0001271183,0.0000851791,0.0001946612,0.00007440856],"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":[0.00160264,0.0006932634,0.1204137,0.0004283172,0.0003921225,0.00124343,0.0001719609,0.4458838,0.0457117,0.005304254,0.01078545,0.3673693],"study_design_scores_gemma":[0.000007041053,0.00003834923,0.001977654,0.000005341324,0.00001218603,0.0000775895,0.00001349401,0.9946979,0.001861225,0.001070536,0.0002308863,0.000007738542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5224594,0.0006673628,0.4687003,0.0003511239,0.0001047739,0.0001675844,0.002030554,0.003406042,0.002112941],"genre_scores_gemma":[0.8761757,0.00019797,0.1200513,0.0001035764,0.00003205618,0.00009937775,0.002383215,0.00009921886,0.0008575286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002514143,"threshold_uncertainty_score":0.004998982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008975504715840538,"score_gpt":0.2198560894298847,"score_spread":0.2108805847140442,"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."}}