{"id":"W1598732405","doi":"","title":"Inverse protein folding in 2D HP mode (extended abstract).","year":2004,"lang":"en","type":"article","venue":"PubMed","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Inverse; Protein folding; Protein structure; Fold (higher-order function); Stability (learning theory); Folding (DSP implementation); Sequence (biology); Protein structure prediction; Protein design; Computer science; Mathematics; Chemistry; Biochemistry; Geometry; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001434388,0.0002800235,0.0002762661,0.0001676104,0.0002953401,0.0005278871,0.00054771,0.0006055136,0.0184107],"category_scores_gemma":[0.0003007015,0.000151716,0.0002952908,0.0002062238,0.0004412941,0.0008391232,0.000565053,0.0004909678,0.002439058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002823163,"about_ca_system_score_gemma":0.0001993047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007274072,"about_ca_topic_score_gemma":0.0005118723,"domain_scores_codex":[0.999954,0.000009167515,0.00000152694,0.00001094213,0.00001968004,0.000004666576],"domain_scores_gemma":[0.9999491,0.00001926999,0.00000683281,0.00001147109,0.00000608215,0.000007264699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004046312,0.0001459275,0.001267728,0.0008317083,0.00008732363,0.001445624,0.0004416225,0.3667184,0.08497549,0.4165965,0.04254114,0.084544],"study_design_scores_gemma":[0.00005581406,0.00006367543,0.0003635125,0.0000138442,0.000008212937,0.0003207589,0.00002841307,0.9074218,0.005215955,0.0658599,0.02062333,0.00002482741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1230703,0.002344853,0.7904695,0.003517532,0.0009881414,0.0001239114,0.002264901,0.002066525,0.07515424],"genre_scores_gemma":[0.7558255,0.002106292,0.1971045,0.0008204027,0.0002043541,0.0003653828,0.001457126,0.0005461048,0.0415705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0184107,"threshold_uncertainty_score":0.06158996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009174723138362675,"score_gpt":0.2209852095889147,"score_spread":0.211810486450552,"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."}}