{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006562399,0.0006411102,0.0007429,0.0005024076,0.0002544044,0.000463496,0.0006724918,0.0006627085,0.001244088],"category_scores_gemma":[0.001841471,0.0003356324,0.0007932482,0.0004894816,0.0002885308,0.0009636667,0.0005173736,0.000888476,0.0006853473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003223188,"about_ca_system_score_gemma":0.0006221448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002538939,"about_ca_topic_score_gemma":0.003296238,"domain_scores_codex":[0.9997979,0.00009033513,0.00001246341,0.00004664431,0.00003334498,0.00001932729],"domain_scores_gemma":[0.9994004,0.0003378858,0.00007680197,0.00009520976,0.0000621928,0.00002753272],"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":[0.0002947905,0.00008171255,0.001535503,0.00004259592,0.00005863131,0.0000600583,0.00001856783,0.9497247,0.007988271,0.001836051,0.0004050769,0.03795403],"study_design_scores_gemma":[0.000004934017,0.00001978652,0.00007777627,0.000001799427,0.000005260576,0.000004805383,0.000001477261,0.9986225,0.0006808685,0.0005173238,0.00006099107,0.000002499802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3195168,0.0006737487,0.6754592,0.0002217526,0.00004925931,0.00004927933,0.0003856893,0.00171606,0.001928268],"genre_scores_gemma":[0.882786,0.0001946471,0.115245,0.00005638568,0.0000254943,0.00005555652,0.0004278933,0.0001452376,0.001063825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002538939,"threshold_uncertainty_score":0.005048275,"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."}}