{"id":"W4242737761","doi":"10.1504/ijbra.2018.094961","title":"Tertiary and quaternary structure prediction of full-length human p53 by comparative modelling with structural environment-based alignment method","year":2018,"lang":"en","type":"article","venue":"International Journal of Bioinformatics Research and Applications","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Homology modeling; Tetramer; Protein quaternary structure; Virtual screening; Computational biology; Protein structure prediction; Protein structure; Stability (learning theory); Protein tertiary structure; Biological system; Artificial intelligence; Drug discovery; Machine learning; Biology; Bioinformatics","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":[0.0004794381,0.0001137982,0.0001467,0.0001860632,0.0001493324,0.00006657827,0.0003170336,0.00008826757,0.00001768271],"category_scores_gemma":[0.00001244742,0.00008258651,0.00003214661,0.00006741613,0.0006734735,0.0000300555,0.0001382367,0.0001964716,0.0000011963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003105794,"about_ca_system_score_gemma":0.00008804256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001426366,"about_ca_topic_score_gemma":0.00000512129,"domain_scores_codex":[0.9983129,0.00005040139,0.0005139104,0.0001190606,0.0008118553,0.0001918581],"domain_scores_gemma":[0.998705,0.00005061343,0.0002626717,0.0001520112,0.0006491989,0.0001804458],"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":[0.001371355,0.0004287742,0.007030015,0.000320872,0.001157134,0.000003506228,0.002502324,0.002264614,0.9274557,0.001199442,0.004463619,0.05180262],"study_design_scores_gemma":[0.00474494,0.008612409,0.00259848,0.0002388337,0.00007376003,0.0003327105,0.003864615,0.3690138,0.5617227,0.003520178,0.04481575,0.0004618372],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5394273,0.0001996544,0.459254,0.0002811455,0.0000286116,0.0002910045,0.0003549876,0.000002401753,0.0001608515],"genre_scores_gemma":[0.9439228,0.0003646055,0.05518783,0.0000464491,0.0002027717,0.00001112771,0.0002168409,0.000007087879,0.00004050378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4044955,"threshold_uncertainty_score":0.336778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03561297071526449,"score_gpt":0.3579837809919926,"score_spread":0.3223708102767281,"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."}}