{"id":"W2163017458","doi":"10.1093/nar/gkm800","title":"PPT-DB: the protein property prediction and testing database","year":2007,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Alberta Prion Research Institute; Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Genome Canada","keywords":"Property (philosophy); Database; Categorical variable; Similarity (geometry); Computer science; Sequence (biology); Protein sequencing; Protein folding; Biology; Protein structure prediction; Sequence alignment; Protein structure; Folding (DSP implementation); Data mining; Bioinformatics; Computational biology; Peptide sequence; Genetics; Artificial intelligence; Machine learning; Engineering","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":[0.004433035,0.003060199,0.002859065,0.003494741,0.0009506874,0.003265121,0.008579002,0.001765404,0.03538115],"category_scores_gemma":[0.009372642,0.001369833,0.001524758,0.003876074,0.0008891599,0.003963288,0.003546739,0.002337712,0.05592408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001069724,"about_ca_system_score_gemma":0.002899391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003021577,"about_ca_topic_score_gemma":0.0020891,"domain_scores_codex":[0.9966689,0.0004056616,0.000461186,0.0006269357,0.001591358,0.0002458913],"domain_scores_gemma":[0.9950537,0.001197749,0.0006147735,0.001225769,0.001297705,0.0006103382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001302792,0.0002892745,0.002941331,0.001509525,0.0001390562,0.0004886111,0.00007834235,0.00273784,0.01095653,0.005809394,0.9001926,0.07355471],"study_design_scores_gemma":[0.001235302,0.0005035471,0.008006047,0.0004777898,0.0001829176,0.001567412,0.0001586452,0.03577993,0.04729191,0.02075615,0.8837795,0.0002608843],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01231316,0.002624535,0.11039,0.001043067,0.00048958,0.001128988,0.5478294,0.2868572,0.03732399],"genre_scores_gemma":[0.01639595,0.001150052,0.05862064,0.0006601057,0.00007387311,0.0007904078,0.9083483,0.008033796,0.005926979],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03538115,"threshold_uncertainty_score":0.1183618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04146996659332628,"score_gpt":0.3218727358760153,"score_spread":0.280402769282689,"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."}}