{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001851448,0.00009800236,0.00006738774,0.00004952573,0.0003661589,0.00005643902,0.000247903,0.0001278246,0.00001038158],"category_scores_gemma":[0.0007802452,0.0000548734,0.00001999509,0.0002272958,0.000299779,0.000007703841,0.0003750295,0.0003538446,0.000008076271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001916213,"about_ca_system_score_gemma":0.00008365411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009057222,"about_ca_topic_score_gemma":0.00005051109,"domain_scores_codex":[0.9986897,0.0001081775,0.0001444766,0.0003231439,0.0003386723,0.0003958775],"domain_scores_gemma":[0.9991862,0.00003666757,0.00002691491,0.0004414099,0.0002116743,0.00009713484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001104496,0.00001891185,0.004946359,0.00002831375,0.0000129772,0.00000762812,0.0000452218,0.000001810159,0.9521213,0.0001979063,0.0008695365,0.04163957],"study_design_scores_gemma":[0.001552066,0.001848467,0.05521183,0.0001483296,0.00002106124,0.0002393793,0.0007595305,0.003917134,0.722909,0.001985134,0.2108754,0.0005325706],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888883,0.0003928205,0.002661751,0.0003395654,0.00003964463,0.0007236058,0.00002673712,0.00002222257,0.006905314],"genre_scores_gemma":[0.9944534,0.00003384455,0.003470027,0.0000797064,0.0003741159,0.00003885233,0.00004506403,0.00002131003,0.001483732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2292123,"threshold_uncertainty_score":0.2816235,"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."}}