{"id":"W2063249983","doi":"10.1287/ijoc.2014.0596","title":"Efficient Use of Semidefinite Programming for Selection of Rotamers in Protein Conformations","year":2014,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Semidefinite embedding; Semidefinite programming; Rounding; Relaxation (psychology); Reduction (mathematics); Mathematical optimization; Computer science; Cutting-plane method; Constraint (computer-aided design); Constraint programming; Theory of computation; Mathematics; Algorithm; Integer programming; Quadratically constrained quadratic program; Stochastic programming; Quadratic programming","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003793956,0.00006911525,0.0001157411,0.00009474979,0.0000495002,0.00002321895,0.00007267114,0.00005453304,0.000001214189],"category_scores_gemma":[0.0002052554,0.00005749463,0.00007718639,0.00008753131,0.0000232246,0.000007162168,0.0000225657,0.00009335865,6.119743e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001300057,"about_ca_system_score_gemma":0.00004616606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000906559,"about_ca_topic_score_gemma":0.000005156661,"domain_scores_codex":[0.9992685,0.0000159778,0.0004254066,0.00006181716,0.00009081007,0.0001375274],"domain_scores_gemma":[0.9993241,0.00003017211,0.0003448467,0.00006434647,0.0002075376,0.00002902464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004567753,0.0001569374,0.009528775,0.0001485266,0.00008036502,3.688296e-7,0.000594553,0.4372114,0.3208518,0.0005865001,0.000112081,0.2302719],"study_design_scores_gemma":[0.00196267,0.003753467,0.005226331,0.0008232954,0.0000166967,0.00005821086,0.0003990859,0.3177845,0.6602531,0.00006064537,0.009360606,0.0003013611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9533607,0.000007638646,0.04626188,0.00001284584,0.00005726156,0.0001936367,0.000001914101,0.000002471692,0.000101644],"genre_scores_gemma":[0.9943925,0.000002783297,0.005471423,0.00005032491,0.00005117068,0.000003770245,0.000007562317,0.000004882322,0.00001554384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3394013,"threshold_uncertainty_score":0.2344563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01886628998821299,"score_gpt":0.2622147740495262,"score_spread":0.2433484840613132,"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."}}