{"id":"W2971157179","doi":"10.1073/pnas.1813495116","title":"Comprehensive genomic profiling of glioblastoma tumors, BTICs, and xenografts reveals stability and adaptation to growth environments","year":2019,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; University of Toronto; University of Calgary; University of British Columbia; Hospital for Sick Children; Canada's Michael Smith Genome Sciences Centre","funders":"Terry Fox Research Institute; Stem Cell Network; Government of Canada; Canadian Institutes of Health Research; Canada's Michael Smith Genome Sciences Centre","keywords":"Biology; Epigenomics; Transcriptome; Glioblastoma; Computational biology; Genomics; Epigenome; Brain tumor; Deep sequencing; Cancer research; Genome; Gene; Genetics; Gene expression; DNA methylation; Medicine; 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":[0.0001059417,0.0001382938,0.0001902219,0.0003137696,0.0001514283,0.0002635882,0.00007225985,0.0001189095,0.0004162827],"category_scores_gemma":[0.0001608625,0.00008913472,0.0001416374,0.0003870053,0.0001079085,0.0001013655,0.0001571847,0.0002553853,0.0001695533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001834421,"about_ca_system_score_gemma":0.0001807568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00157972,"about_ca_topic_score_gemma":0.003168538,"domain_scores_codex":[0.9999104,0.00000831602,0.00000519815,0.00002530914,0.00003196548,0.00001884015],"domain_scores_gemma":[0.9999167,0.00001318613,0.00002232771,0.00001172146,0.00001921068,0.00001688016],"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":[0.00009600019,0.00001135476,0.005712416,0.0000189747,0.00001098576,0.00003045887,0.00003263001,0.0001771315,0.9905807,0.00003977908,0.00008784213,0.003201663],"study_design_scores_gemma":[0.00001270044,0.0005569077,0.4987514,0.00001164452,0.00009457906,0.0009091699,0.0003204329,0.003517396,0.4881875,0.0002473532,0.007373897,0.00001712066],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914063,0.0006579583,0.003735417,0.00004295215,0.000006908419,0.00002089328,0.003266548,0.00009294535,0.0007700893],"genre_scores_gemma":[0.981598,0.00104344,0.004511598,0.00006451784,0.00000466864,0.00005662003,0.01154182,0.00005891966,0.001120436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00157972,"threshold_uncertainty_score":0.003140986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03919365574144938,"score_gpt":0.2814875110776112,"score_spread":0.2422938553361618,"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."}}