{"id":"W2594767287","doi":"10.3389/fgene.2017.00029","title":"Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores","year":2017,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"WSP (Canada); York University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Voronoi diagram; Pairwise comparison; Computational biology; Disease; Gene; Gene regulatory network; Phenotype; Computer science; Data mining; Biology; Genetics; Artificial intelligence; Medicine; Mathematics; Gene expression; Pathology","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":[0.0009360741,0.000430362,0.0006082921,0.004453286,0.0004507988,0.001497547,0.0007379414,0.0004954106,0.001292673],"category_scores_gemma":[0.005391081,0.000205796,0.0007754705,0.002078391,0.0005766862,0.0008977759,0.001163839,0.0003277877,0.0002121902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009013179,"about_ca_system_score_gemma":0.0006030067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007511853,"about_ca_topic_score_gemma":0.005217744,"domain_scores_codex":[0.9991886,0.0002459944,0.00004500303,0.0001975906,0.0002448265,0.00007796915],"domain_scores_gemma":[0.9982754,0.000980761,0.0002679009,0.0001041578,0.0002735852,0.00009827196],"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":[0.000422462,0.00008664385,0.1143688,0.0002507781,0.0004501351,0.0007571756,0.0005578163,0.6456043,0.01442039,0.09896713,0.002004458,0.1221099],"study_design_scores_gemma":[0.00001655385,0.00003665078,0.01574201,0.00002519344,0.00007003472,0.0003828052,0.0001089014,0.9376726,0.00204422,0.04180143,0.002066882,0.00003268617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1558705,0.0007194726,0.8389253,0.0002451244,0.00002652059,0.00008715808,0.0009158057,0.0004120063,0.002798196],"genre_scores_gemma":[0.8627645,0.000403613,0.1349614,0.000034107,0.00003898996,0.00008606983,0.000822632,0.00005309207,0.000835514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007511853,"threshold_uncertainty_score":0.01493627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009498318831690295,"score_gpt":0.2615549959960338,"score_spread":0.2520566771643435,"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."}}