{"id":"W2136671508","doi":"10.1186/1471-2105-11-507","title":"Proteome scanning to predict PDZ domain interactions using support vector machines","year":2010,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Hippo pathway signaling and YAP/TAZ","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"PDZ domain; Computational biology; Proteome; Protein–protein interaction; Support vector machine; DNA microarray; Domain (mathematical analysis); Computer science; Protein Array Analysis; Biology; Human proteome project; Proteomics; Bioinformatics; Artificial intelligence; Genetics; Gene; Gene expression; Mathematics","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":[0.0002801463,0.0001845339,0.0001473408,0.0001027258,0.000172373,0.00007578386,0.0002195079,0.0001384953,0.00008097514],"category_scores_gemma":[0.0001568957,0.0001664383,0.00009745733,0.0001362764,0.00005044757,0.00001663952,0.000126314,0.0002142121,0.0001045938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001393225,"about_ca_system_score_gemma":0.0001779415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000253081,"about_ca_topic_score_gemma":0.0001098797,"domain_scores_codex":[0.998976,0.00002126901,0.0003690071,0.0001699261,0.0001597401,0.000304076],"domain_scores_gemma":[0.9992014,0.0000128945,0.0001189457,0.0003875229,0.00009228163,0.0001869116],"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":[0.00003889547,0.00003349131,0.002878389,0.0000684452,0.00002431991,0.000001320339,0.0006287702,0.0006062674,0.9941168,0.00003238725,0.0004728845,0.001098064],"study_design_scores_gemma":[0.001792261,0.00112435,0.005209227,0.000208107,0.0001063043,0.0005808474,0.001201094,0.1548511,0.6013646,0.0001610192,0.2317839,0.001617137],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8983578,0.00000878379,0.09845807,0.00005323244,0.0005906958,0.0003453025,0.00007777536,0.00004650223,0.002061807],"genre_scores_gemma":[0.6416087,0.000001578003,0.3565834,0.0003175052,0.0006078146,0.00004173705,0.000178179,0.00003504038,0.0006260507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3927521,"threshold_uncertainty_score":0.6787159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01707310936846593,"score_gpt":0.2779552817931932,"score_spread":0.2608821724247273,"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."}}