{"id":"W2049624178","doi":"10.1186/gm433","title":"Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction","year":2013,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Ontario Institute for Cancer Research","keywords":"Human genetics; Computational biology; Gene expression; Systems biology; Gene; Proteomics; Gene expression profiling; Bioinformatics; Biology; Computer science; Genetics","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":[0.0002529871,0.0001263523,0.0001493659,0.000179154,0.00003785542,0.00000908585,0.0001945644,0.00006754771,0.0001164734],"category_scores_gemma":[0.0001031254,0.0001048975,0.00002340211,0.0002163701,0.00006497716,0.000008345321,0.00006503377,0.00006459624,0.00001596652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002597871,"about_ca_system_score_gemma":0.0000724499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001860187,"about_ca_topic_score_gemma":0.000001182425,"domain_scores_codex":[0.998665,0.00006008689,0.0004208846,0.0003541214,0.0003268143,0.0001731037],"domain_scores_gemma":[0.9993008,0.00001099525,0.0001659611,0.0002423527,0.0001761042,0.0001038002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003713659,0.00008647922,0.01752916,0.00003114363,0.000007836479,2.977695e-7,0.0002302858,0.0007156932,0.9771261,0.00001087574,0.001909424,0.002315517],"study_design_scores_gemma":[0.0008108124,0.0005696933,0.4618995,0.00006585704,0.00001137466,0.000001977658,0.0002765838,0.0001724367,0.5349572,0.0001004802,0.00102591,0.000108199],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903854,0.0002352108,0.007507308,0.000535181,0.0001297925,0.00101199,0.00003595882,0.00001162024,0.0001475238],"genre_scores_gemma":[0.9953994,0.00002161257,0.00256137,0.0002531391,0.0002059563,0.0004760812,0.0009166711,0.00001508387,0.0001506881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4443703,"threshold_uncertainty_score":0.4277597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464467969579701,"score_gpt":0.2524387292195094,"score_spread":0.2377940495237124,"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."}}