{"id":"W2110186020","doi":"10.1142/s0219720009004345","title":"PREDICTING LOCAL QUALITY OF A SEQUENCE–STRUCTURE ALIGNMENT","year":2009,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science and Technology of the People's Republic of China","keywords":"Protein structure prediction; Threading (protein sequence); Computer science; Support vector machine; CASP; Sequence (biology); Artificial intelligence; Quality (philosophy); Data mining; Homology modeling; Local structure; Similarity (geometry); Loop modeling; Smith–Waterman algorithm; Protein structure; Sequence alignment; Biology; Peptide sequence","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":[0.0002109201,0.00008418598,0.0001878633,0.00004946458,0.000034053,0.000007977384,0.00009726535,0.0001101494,0.000002708885],"category_scores_gemma":[0.00004749955,0.00006321438,0.0000641731,0.00004302326,0.0001049633,0.000007753501,0.00003040631,0.00008100721,1.126711e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009230802,"about_ca_system_score_gemma":0.00008689974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002618222,"about_ca_topic_score_gemma":0.000001466022,"domain_scores_codex":[0.9991398,0.00003194303,0.0005651721,0.00005902009,0.0001096228,0.00009438669],"domain_scores_gemma":[0.9991617,0.00002355928,0.0005153064,0.00006358352,0.0001839091,0.00005194666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001160211,0.0001938331,0.0395286,0.0002831894,0.0006576446,0.000007877579,0.001217464,0.06088278,0.4725067,0.05623175,0.0004809347,0.366849],"study_design_scores_gemma":[0.009403285,0.0163463,0.178341,0.0002668533,0.0002090806,0.003078966,0.002041264,0.2230837,0.07254907,0.488279,0.004970958,0.001430513],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8530592,0.0003037249,0.1461732,0.000163223,0.0000739511,0.00005098355,0.00004891277,0.000001556924,0.0001252027],"genre_scores_gemma":[0.9685383,0.00004991316,0.03089924,0.0003732056,0.00007734058,1.79799e-7,0.00005707212,0.000002016114,0.000002708072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4320472,"threshold_uncertainty_score":0.2577808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022403419759427,"score_gpt":0.2814112386253455,"score_spread":0.2711872044277512,"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."}}