{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001762098,0.001222624,0.001503667,0.0007276093,0.0006273217,0.001131485,0.00180826,0.001123764,0.005343844],"category_scores_gemma":[0.003394071,0.0008903358,0.001345102,0.0008499812,0.0009219614,0.001534257,0.001555115,0.002358018,0.00179462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009440032,"about_ca_system_score_gemma":0.001898842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002971896,"about_ca_topic_score_gemma":0.003039276,"domain_scores_codex":[0.9988731,0.000428366,0.00003937602,0.0001960939,0.000326645,0.0001364344],"domain_scores_gemma":[0.9986939,0.000893097,0.0001077568,0.0001034853,0.0001289333,0.00007289206],"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":[0.000182637,0.0001565231,0.0004511532,0.0001907405,0.00006534307,0.00009346699,0.0001014841,0.8191842,0.003436257,0.03860606,0.01020589,0.1273261],"study_design_scores_gemma":[0.00001995884,0.00002907222,0.00001746421,0.000004764162,0.00000379877,0.00001325189,0.000005302898,0.9911737,0.0004554061,0.007018482,0.001253997,0.000004783802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006034249,0.0002587446,0.9881334,0.0002332332,0.0000452459,0.00005423913,0.00007130314,0.002195042,0.002974596],"genre_scores_gemma":[0.1117465,0.0003258982,0.8816873,0.0002746763,0.00008781006,0.0003781466,0.0003771976,0.0008764931,0.004246027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005343844,"threshold_uncertainty_score":0.01787698,"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."}}