{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009205933,0.0009079008,0.0006118728,0.0009515356,0.0002414093,0.000458162,0.0005784175,0.0007609287,0.001336251],"category_scores_gemma":[0.001856292,0.0002589093,0.0007163638,0.0006188697,0.000171836,0.0003821074,0.0003081153,0.0008555386,0.0005722261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004525649,"about_ca_system_score_gemma":0.000527787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913821,"about_ca_topic_score_gemma":0.00116723,"domain_scores_codex":[0.9995951,0.00009228656,0.0000447616,0.0001117413,0.00009743017,0.00005855717],"domain_scores_gemma":[0.9987481,0.0007827433,0.0001162464,0.0000475702,0.0002542742,0.00005104784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001237149,0.0009327158,0.0421499,0.0004305708,0.0004108713,0.0004089439,0.00009570946,0.4963917,0.04839967,0.000993831,0.006228085,0.4023209],"study_design_scores_gemma":[0.000008655082,0.00006629354,0.001662242,0.000003834758,0.00001017586,0.00003703191,0.000006965838,0.9932159,0.004495215,0.0002758843,0.0002133624,0.00000449078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6030315,0.0007488178,0.3866761,0.0003519111,0.00007910084,0.0002251557,0.001774055,0.005946971,0.001166445],"genre_scores_gemma":[0.8757207,0.0001387761,0.1207402,0.00008696932,0.00002964247,0.0002167198,0.002323286,0.00004504497,0.0006985906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001913821,"threshold_uncertainty_score":0.004868627,"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."}}