{"id":"W1967609592","doi":"10.1186/1471-2105-9-388","title":"Sequence based residue depth prediction using evolutionary information and predicted secondary structure","year":2008,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Nankai University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computational biology; DNA microarray; Sequence (biology); Computer science; Bioinformatics; Biology; Artificial intelligence; Data mining; Genetics; Gene; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006278274,0.0001694896,0.0001177729,0.00009207104,0.0002308138,0.00002761822,0.0001138209,0.0002665836,0.00001005858],"category_scores_gemma":[0.00008603362,0.0001583196,0.00004104765,0.0001240386,0.0001524049,0.00008613351,0.0000787879,0.0001442228,0.000001623711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003660375,"about_ca_system_score_gemma":0.0004727741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001768551,"about_ca_topic_score_gemma":0.00002789026,"domain_scores_codex":[0.9990887,0.00002634135,0.000377852,0.0001167785,0.0001929116,0.0001974342],"domain_scores_gemma":[0.9993242,0.000009373906,0.0001806127,0.0002703714,0.0001270418,0.00008839322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002134644,0.0001521807,0.6173203,0.004280345,0.0004619193,0.00002351684,0.003616789,0.1707771,0.1388659,0.001261882,0.02198326,0.03912217],"study_design_scores_gemma":[0.001143278,0.0002153251,0.08573843,0.00003745154,0.00003096529,0.0005165128,0.0001280557,0.9000261,0.007264032,0.0001914911,0.004398534,0.000309784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7525606,0.0002228783,0.24486,0.00001614464,0.000179184,0.0004025251,0.0009937779,0.00006028264,0.0007046481],"genre_scores_gemma":[0.7380805,0.00007616919,0.2587219,0.0003910107,0.0001283708,0.000007351292,0.002560083,0.00001270257,0.00002195129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7292491,"threshold_uncertainty_score":0.6456088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357629204791013,"score_gpt":0.2230453381615906,"score_spread":0.2094690461136804,"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."}}