{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001602084,0.000145492,0.0004878651,0.00009554883,0.00005955116,0.000001308557,0.00003319415,0.0001247731,0.0000194129],"category_scores_gemma":[0.0002983753,0.00007304391,0.00004662109,0.00004433507,0.0004985472,0.00001834483,0.00002540416,0.0000333737,0.000001017635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001912379,"about_ca_system_score_gemma":0.00001827084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003074757,"about_ca_topic_score_gemma":0.000007043202,"domain_scores_codex":[0.9991988,0.00002826593,0.0002471203,0.0002535749,0.00008057868,0.0001916561],"domain_scores_gemma":[0.9991112,0.0003769547,0.0001092622,0.0001207208,0.0001245744,0.0001573173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009969607,0.0004152609,0.9476392,0.0003517057,0.0004035289,0.000003848681,0.0008784888,0.000003147118,0.009572379,0.006827338,0.004910333,0.02799783],"study_design_scores_gemma":[0.258802,0.05381938,0.292381,0.01154725,0.007344958,0.0003351244,0.003926992,0.01273874,0.03206598,0.009512101,0.315652,0.00187449],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889347,0.005197154,0.00114731,0.00370211,0.0001053588,0.0006110187,0.0001783323,0.00002088237,0.0001031665],"genre_scores_gemma":[0.9973246,0.001568082,0.0001568573,0.0002438135,0.00007721774,0.0002296677,0.00004443662,0.00001382967,0.0003415208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6552581,"threshold_uncertainty_score":0.2978644,"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."}}