{"id":"W1983295588","doi":"10.1007/s00726-014-1817-9","title":"Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models","year":2014,"lang":"en","type":"article","venue":"Amino Acids","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Particle swarm optimization; Embedding; Linear regression; Computer science; Algorithm; Benchmark (surveying); Mathematics; Regression; Artificial intelligence; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002005661,0.0002868259,0.0004430335,0.0002027191,0.0001707179,0.0001056397,0.0009811268,0.0001547351,0.000008149374],"category_scores_gemma":[0.0004704382,0.0002929115,0.0001773064,0.000538019,0.000144398,0.001168296,0.0003869149,0.0002523646,0.000005506372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001735013,"about_ca_system_score_gemma":0.0001657265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001632497,"about_ca_topic_score_gemma":0.000005216202,"domain_scores_codex":[0.9969074,0.0006037849,0.000713931,0.0008232563,0.0005654073,0.0003862764],"domain_scores_gemma":[0.9975082,0.0007186187,0.000312567,0.001035268,0.0002740734,0.0001512598],"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":[0.0001510918,0.0002406327,0.0004706451,0.0001586611,0.00005457983,0.000001532888,0.00108978,0.86485,0.09096768,0.008268905,0.0002237612,0.03352274],"study_design_scores_gemma":[0.001005415,0.0001928526,0.0007702648,0.0000453541,0.00001668243,0.000002083227,0.00001710671,0.8752933,0.1124309,0.009959667,0.00005850017,0.0002078883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1800141,0.00006926673,0.8185108,0.0002050249,0.0003337549,0.0002714031,0.00002028238,0.0002328327,0.0003425117],"genre_scores_gemma":[0.5987156,0.000002045131,0.400966,0.0001412893,0.00006920653,0.00002535463,0.00003622978,0.00001570445,0.00002862671],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4187014,"threshold_uncertainty_score":0.9999523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02634237728520585,"score_gpt":0.2985845917005217,"score_spread":0.2722422144153159,"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."}}