{"id":"W2011692099","doi":"10.1371/journal.pone.0094507","title":"Prediction and Experimental Characterization of nsSNPs Altering Human PDZ-Binding Motifs","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"PDZ domain; Computational biology; Genetics; Biology; Single-nucleotide polymorphism; Phenotype; In silico; Binding site; Plasma protein binding; Protein domain; Gene; Bioinformatics; Genotype; Cell biology","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":[0.000718154,0.0004154152,0.0004068515,0.000477107,0.0003111627,0.0003549047,0.0004075786,0.0005138972,0.001333827],"category_scores_gemma":[0.001741143,0.0001999702,0.0005358079,0.0004463239,0.0003169493,0.0002561036,0.0002430242,0.00060783,0.0004864633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003532242,"about_ca_system_score_gemma":0.0004000779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001231065,"about_ca_topic_score_gemma":0.0017946,"domain_scores_codex":[0.9996071,0.00009038658,0.00004478212,0.0001215101,0.0001025029,0.00003372472],"domain_scores_gemma":[0.9990139,0.0005485518,0.0001689471,0.000118303,0.0000906851,0.00005962898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001290611,0.0006955093,0.04306868,0.0005984078,0.0001534911,0.0007263322,0.00006486941,0.07304734,0.8373371,0.001776286,0.001031446,0.04020979],"study_design_scores_gemma":[0.0001179648,0.0007896067,0.03490959,0.00002340779,0.00009927692,0.001042044,0.00007300846,0.3995683,0.5584595,0.001522252,0.003360531,0.00003460602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667925,0.0005014134,0.02976727,0.0001148102,0.0000165098,0.00006844622,0.001638869,0.0002682823,0.0008318329],"genre_scores_gemma":[0.9112805,0.0004702477,0.0800625,0.00008697744,0.00001095583,0.00009843259,0.007426762,0.00005044932,0.0005130836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001333827,"threshold_uncertainty_score":0.004462123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885792679175896,"score_gpt":0.2278973181397918,"score_spread":0.2090393913480329,"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."}}