{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015527,0.0004565323,0.0008225893,0.002883712,0.0002586483,0.0006744495,0.0005640204,0.0005919856,0.001062356],"category_scores_gemma":[0.005031426,0.0002450954,0.0005695731,0.001322488,0.0003079121,0.001115376,0.0005122864,0.0004880971,0.0006701812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004266422,"about_ca_system_score_gemma":0.0004573127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002112957,"about_ca_topic_score_gemma":0.002783339,"domain_scores_codex":[0.9991888,0.0001301481,0.00007858766,0.0002035369,0.0003276948,0.0000712058],"domain_scores_gemma":[0.9954211,0.001544609,0.001206296,0.0003870573,0.001126242,0.0003145748],"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":[0.001561969,0.0004285731,0.3465326,0.0004115416,0.0004152641,0.0006822518,0.0002950611,0.2092888,0.2023021,0.001632318,0.00340163,0.2330478],"study_design_scores_gemma":[0.00002384396,0.0003175672,0.05795766,0.00001721844,0.0000476518,0.0002402885,0.00009211493,0.9140648,0.02581296,0.0008657975,0.0005297159,0.00003047279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.852674,0.000614566,0.1429739,0.00007607402,0.00001647895,0.00004252042,0.0006069597,0.002228313,0.0007671474],"genre_scores_gemma":[0.9526821,0.0001329583,0.04542695,0.00001987339,0.00001210753,0.00001759661,0.001359367,0.00008541765,0.0002636301],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002883712,"threshold_uncertainty_score":0.008211553,"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."}}