{"id":"W2143633031","doi":"10.1109/icmla.2006.27","title":"Impact of the Predicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins","year":2006,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sequence (biology); Representation (politics); Protein structure prediction; Protein sequencing; Protein secondary structure; Computer science; Artificial intelligence; In silico; Class (philosophy); Pattern recognition (psychology); Protein structure; Alpha (finance); Mathematics; Peptide sequence; Biology; Statistics; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001103106,0.0004030041,0.0005192547,0.000807312,0.0002398199,0.0005427589,0.000291514,0.0004759986,0.0005428466],"category_scores_gemma":[0.003894404,0.0001265486,0.0003159209,0.00034098,0.00020621,0.0006589442,0.0003634923,0.0004354529,0.0002682288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003354371,"about_ca_system_score_gemma":0.0003246772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001208751,"about_ca_topic_score_gemma":0.001489618,"domain_scores_codex":[0.9995241,0.0001779818,0.00002429839,0.00009821807,0.0001237836,0.00005159687],"domain_scores_gemma":[0.9975592,0.001797253,0.000263571,0.000135068,0.0001523974,0.00009245078],"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":[0.006032712,0.0007556126,0.1353506,0.0002553981,0.0002818708,0.0003267137,0.0002479479,0.3567821,0.1546348,0.0009217114,0.00154524,0.3428653],"study_design_scores_gemma":[0.00008633277,0.0008047551,0.03199128,0.00001964154,0.00006609049,0.0002394117,0.00009653166,0.9211926,0.04443023,0.0006261203,0.0004206939,0.00002640353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886973,0.00008349518,0.0105513,0.00004879883,0.000004275991,0.00001039506,0.0001392567,0.0002442491,0.0002209405],"genre_scores_gemma":[0.9851462,0.00006527596,0.01350924,0.00002172559,0.00000517057,0.00001352426,0.001008928,0.0000250981,0.0002047347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001208751,"threshold_uncertainty_score":0.005833864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096550922401967,"score_gpt":0.2534153032325261,"score_spread":0.2424497940085064,"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."}}