{"id":"W4389166922","doi":"10.1371/journal.pone.0294750","title":"BioDiscViz: A visualization support and consensus signature selector for BioDiscML results","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier de l'Université Laval; Hôtel-Dieu de Québec; Université Laval","funders":"Canadian Institutes of Health Research","keywords":"Overfitting; Computer science; Visualization; Bootstrapping (finance); Machine learning; Field (mathematics); Flexibility (engineering); Data mining; Artificial intelligence; Computer graphics; Data science; Statistics","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":[0.005546168,0.003730421,0.001925949,0.006555856,0.0009932162,0.004068327,0.003606702,0.001998475,0.07732468],"category_scores_gemma":[0.01860514,0.00121811,0.00273279,0.00265341,0.0008199012,0.003914329,0.004356024,0.002620394,0.01969894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0015261,"about_ca_system_score_gemma":0.002010643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003964575,"about_ca_topic_score_gemma":0.003772509,"domain_scores_codex":[0.9981931,0.0003726531,0.0002254527,0.0003564464,0.0006453293,0.0002070492],"domain_scores_gemma":[0.9918436,0.005000204,0.0005822864,0.0008414316,0.001403665,0.0003286892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002668665,0.0003870513,0.006648992,0.003276226,0.0004211387,0.001520991,0.001860721,0.01331063,0.02134644,0.021665,0.6545279,0.2723663],"study_design_scores_gemma":[0.001138439,0.0003453272,0.007051031,0.001380288,0.0002438918,0.001376625,0.0006744281,0.3396886,0.09470122,0.06583188,0.4869087,0.0006595898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.005036595,0.000314627,0.3472273,0.0008349164,0.0003060486,0.0002670338,0.02955831,0.6111366,0.005318552],"genre_scores_gemma":[0.08393991,0.0008316826,0.674656,0.001794755,0.0002535566,0.002601368,0.07096944,0.1534948,0.01145838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.07732468,"threshold_uncertainty_score":0.2586768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04758482582192739,"score_gpt":0.2839674409790853,"score_spread":0.2363826151571579,"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."}}