{"id":"W2323755120","doi":"10.1186/s13029-016-0053-y","title":"MM2S: personalized diagnosis of medulloblastoma patients and model systems","year":2016,"lang":"en","type":"article","venue":"Source Code for Biology and Medicine","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Fondation Brain Canada","keywords":"Medulloblastoma; Computer science; Classifier (UML); Computational biology; Genomics; Identification (biology); R package; Sample (material); Data mining; Bioinformatics; Artificial intelligence; Biology; Medicine; Gene; Pathology; Genetics; Genome","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":[0.008359212,0.001892682,0.001423245,0.003896421,0.0008655116,0.002427043,0.002500431,0.00127092,0.01378326],"category_scores_gemma":[0.0216399,0.001166742,0.005378481,0.001849861,0.0005879071,0.001313179,0.002220159,0.001638616,0.004998908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001588598,"about_ca_system_score_gemma":0.002514979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007066956,"about_ca_topic_score_gemma":0.01149185,"domain_scores_codex":[0.9968255,0.00120927,0.0002109929,0.001084891,0.0005342977,0.000135147],"domain_scores_gemma":[0.9921108,0.005098228,0.0008173572,0.001193114,0.0005074215,0.0002731005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004138588,0.0003682992,0.3084954,0.003752618,0.01009637,0.001198388,0.00105777,0.1061478,0.0136541,0.01770777,0.265793,0.2675899],"study_design_scores_gemma":[0.001121456,0.000801324,0.07318398,0.0009317431,0.004328318,0.002161714,0.0005258773,0.5229291,0.02795117,0.09300761,0.2726207,0.0004370748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07982807,0.004455842,0.4933653,0.004975803,0.0008785013,0.0007324549,0.2555506,0.1514537,0.008759715],"genre_scores_gemma":[0.3251597,0.001766395,0.426069,0.002266695,0.0004091192,0.00204445,0.2191239,0.01822341,0.004937323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01378326,"threshold_uncertainty_score":0.04610968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101600325742787,"score_gpt":0.294472485463703,"score_spread":0.2734564822062751,"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."}}