{"id":"W2796148592","doi":"10.1101/293159","title":"TranCEP: Predicting transmembrane transport proteins using composition, evolutionary, and positional information","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Transmembrane protein; Proteome; Computational biology; Membrane protein; Genome; Transport protein; Biology; Transmembrane domain; Amino acid; Membrane transport; Membrane; Membrane transport protein; Biochemistry; Chemistry; 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":[0.001353911,0.001219619,0.0009144477,0.00233572,0.0004022211,0.001218729,0.0009700306,0.001001814,0.002379563],"category_scores_gemma":[0.002334991,0.0002832926,0.0006852559,0.001463092,0.0002693261,0.001173128,0.0007813229,0.0009082093,0.001141541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004810243,"about_ca_system_score_gemma":0.0008907422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004177353,"about_ca_topic_score_gemma":0.004485805,"domain_scores_codex":[0.9995554,0.0001151643,0.00002819235,0.0001460901,0.0001107931,0.00004423932],"domain_scores_gemma":[0.9983503,0.0009116547,0.0001292405,0.0001693423,0.0002841516,0.0001553603],"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":[0.003876146,0.001177986,0.1139889,0.0008146804,0.001125619,0.0008929234,0.0001596135,0.3491305,0.08467773,0.004141662,0.04754928,0.3924651],"study_design_scores_gemma":[0.00004794633,0.0001864018,0.004984563,0.0000128792,0.00004919854,0.0001631764,0.00004108795,0.9794775,0.01218903,0.001102617,0.001723737,0.00002179731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8419886,0.003375288,0.118288,0.0006950122,0.0002228082,0.0001612576,0.01481077,0.01701587,0.003442532],"genre_scores_gemma":[0.8192429,0.0009289998,0.1477243,0.0002373824,0.0001226308,0.0001260939,0.02887078,0.0003566701,0.002390302],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004177353,"threshold_uncertainty_score":0.008306086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006308556193433299,"score_gpt":0.2109663616667824,"score_spread":0.2046578054733491,"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."}}