{"id":"W2082953923","doi":"10.1109/tcbb.2012.54","title":"Reduced False Positives in PDZ Binding Prediction Using Sequence and Structural Descriptors","year":2012,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Plant biochemistry and biosynthesis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Klaus Tschira Stiftung; University of Toronto","keywords":"PDZ domain; Benchmark (surveying); False positive paradox; Sequence (biology); Matthews correlation coefficient; Binary number; Computer science; Data mining; Filter (signal processing); Pattern recognition (psychology); Algorithm; Artificial intelligence; Computational biology; Mathematics; Statistics; Biology; Genetics; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001483105,0.0001536883,0.0001220515,0.00009817521,0.0001963649,0.0000201838,0.00007181911,0.0002088477,0.000005427206],"category_scores_gemma":[0.00002670885,0.0001364291,0.00003340131,0.00009338161,0.0001686912,0.00003505962,0.000009336754,0.0001217104,0.000001525081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002252352,"about_ca_system_score_gemma":0.00003508505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009515868,"about_ca_topic_score_gemma":0.000004220061,"domain_scores_codex":[0.9992713,0.00004525997,0.0002467638,0.0001571613,0.00006169618,0.0002178124],"domain_scores_gemma":[0.9996545,0.00006284353,0.00007534264,0.00009086272,0.00003316236,0.00008326175],"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":[0.0002140207,0.00007293716,0.01650117,0.00007873812,0.0001050039,0.000001063335,0.0004113204,0.003132798,0.9661438,0.0000592472,0.00001496989,0.01326496],"study_design_scores_gemma":[0.00159435,0.0006139415,0.03230294,0.0002124881,0.0001230954,0.001036086,0.001163143,0.07874005,0.8823311,0.0006828652,0.0002963076,0.0009035899],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796857,0.0001431837,0.01938135,0.00006239661,0.0001970784,0.0001094373,0.0003766715,0.00001326954,0.00003088983],"genre_scores_gemma":[0.9839718,0.000101018,0.01550499,0.00009898828,0.00006597499,0.000004742143,0.0002312495,0.000005420381,0.0000158038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08381262,"threshold_uncertainty_score":0.5563419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02964204572160651,"score_gpt":0.2846624902554984,"score_spread":0.2550204445338919,"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."}}