{"id":"W2160672912","doi":"10.1142/s0219720003000186","title":"RAPTOR: OPTIMAL PROTEIN THREADING BY LINEAR PROGRAMMING","year":2003,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":294,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Threading (protein sequence); Linear programming; Computer science; Pairwise comparison; Integer programming; Protein structure prediction; Server; Benchmark (surveying); Algorithm; Theoretical computer science; Mathematical optimization; Protein structure; Artificial intelligence; Mathematics; Biology; Computer network","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":[0.0002576631,0.0001061672,0.000151256,0.00005288361,0.00007044991,0.00002337907,0.0001163795,0.0001170394,0.000004038567],"category_scores_gemma":[0.000102995,0.00008328072,0.00006186367,0.00005031105,0.00008219203,0.00001083333,0.00004283708,0.0001070999,9.980672e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007866383,"about_ca_system_score_gemma":0.0000889607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.784724e-7,"about_ca_topic_score_gemma":2.845483e-7,"domain_scores_codex":[0.9992791,0.00003336788,0.000383409,0.00007567245,0.00008396312,0.0001444369],"domain_scores_gemma":[0.9993857,0.00001575014,0.0002982632,0.00007852899,0.0001479113,0.00007382937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00167993,0.0007151511,0.01352733,0.0006026555,0.002141629,0.00003098168,0.001715655,0.03206709,0.49441,0.1415784,0.01057184,0.3009593],"study_design_scores_gemma":[0.0114767,0.0165782,0.0008084236,0.00026744,0.0002217833,0.005018349,0.001922018,0.0974772,0.129699,0.07070688,0.6634021,0.002421899],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6413356,0.001180445,0.3567276,0.0001287255,0.0001121436,0.0001563282,0.00001830224,0.000004258682,0.0003366068],"genre_scores_gemma":[0.7163395,0.00005803863,0.2832508,0.0001691749,0.00007875715,0.000002776936,0.0000635551,0.000006907958,0.00003049713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6528302,"threshold_uncertainty_score":0.3396089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005385294288186872,"score_gpt":0.2418866384724174,"score_spread":0.2365013441842305,"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."}}