{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002526293,0.00005131787,0.00004948036,0.00002485181,0.00001685381,0.000003305314,0.0000905295,0.00007242087,0.00004510051],"category_scores_gemma":[0.00004509491,0.00004411392,0.0000290518,0.0000473965,0.00002876852,0.00000114107,0.00002908999,0.00004310007,0.00001441121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003181897,"about_ca_system_score_gemma":0.0000173339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007590829,"about_ca_topic_score_gemma":0.00001365118,"domain_scores_codex":[0.9995726,0.000009153969,0.0001726515,0.00007347923,0.00007788058,0.00009428358],"domain_scores_gemma":[0.9996337,0.000005752815,0.0000847402,0.0001993424,0.00004790681,0.00002856688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001802996,0.00002850788,0.002517872,0.00003530597,0.000004057043,9.702536e-8,0.00002741138,0.00001404183,0.9956052,0.0005115971,0.0002718339,0.0009660625],"study_design_scores_gemma":[0.0001634458,0.0001045649,0.0114918,0.000002227776,0.000002981444,0.000001903219,0.00005985709,0.0003992277,0.971717,0.000008560737,0.01598941,0.00005900174],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7860258,0.00001993092,0.05394053,0.00004608543,0.000034066,0.0001337216,0.000001486543,0.00001091755,0.1597874],"genre_scores_gemma":[0.974478,0.000007584503,0.02265057,0.00006854437,0.00004182363,0.000001958274,0.00004038883,0.000006190146,0.00270496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1884522,"threshold_uncertainty_score":0.1798913,"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."}}