{"id":"W2177049649","doi":"10.1371/journal.pone.0139656","title":"Prioritizing Clinically Relevant Copy Number Variation from Genetic Interactions and Gene Function Data","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"Copy-number variation; Context (archaeology); Prioritization; Computational biology; Gene dosage; Gene; Bioinformatics; Comparative genomic hybridization; Disease; Biology; Genetics; Computer science; Genome; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002488077,0.001675017,0.001548675,0.006092187,0.0005020782,0.001247569,0.001107423,0.001209573,0.002498632],"category_scores_gemma":[0.008277182,0.0003267032,0.001222766,0.002813928,0.0002822727,0.0005646699,0.0009229954,0.0008997456,0.001183838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006771762,"about_ca_system_score_gemma":0.001010799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005568871,"about_ca_topic_score_gemma":0.01163206,"domain_scores_codex":[0.9983012,0.0003364966,0.0001640151,0.0006399191,0.0004376167,0.0001208399],"domain_scores_gemma":[0.9965153,0.002523465,0.0002545363,0.0002921907,0.0003172481,0.00009714258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00216543,0.0004606498,0.394956,0.001432973,0.001087353,0.002469902,0.0002416294,0.03202855,0.09927633,0.002206315,0.05377806,0.4098968],"study_design_scores_gemma":[0.0004675105,0.0003990945,0.4042726,0.0001778042,0.001040094,0.007093029,0.0002552313,0.4415873,0.07171059,0.02546153,0.04733301,0.000202177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6717664,0.00721332,0.2014381,0.001712085,0.0002569592,0.0004882093,0.09969305,0.0123143,0.005117557],"genre_scores_gemma":[0.7236238,0.001163615,0.1643283,0.0003946748,0.0002134843,0.0003165901,0.1074867,0.000623672,0.00184924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006092187,"threshold_uncertainty_score":0.01315838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07787416884742374,"score_gpt":0.2725786703620477,"score_spread":0.194704501514624,"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."}}