{"id":"W4388588565","doi":"10.1093/neuonc/noad179.0473","title":"EPCO-09. CHARACTERIZING THE GBM CELLULAR LANDSCAPE BY LARGE-SCALE SINGLE-NUCLEUS RNA-SEQUENCING","year":2023,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Biology; Computational biology; Exome sequencing; RNA; Deep sequencing; Exome; Cell type; Tumor microenvironment; Genome; Cell; Gene; Genetics; Phenotype; Cancer","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002523208,0.001405301,0.0009691959,0.001536513,0.000794219,0.001823026,0.001400773,0.001323252,0.02061068],"category_scores_gemma":[0.002310815,0.0005696917,0.0008781503,0.00134509,0.0004284424,0.0008303333,0.002060191,0.001009404,0.02879812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007787406,"about_ca_system_score_gemma":0.002174213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003653551,"about_ca_topic_score_gemma":0.005974108,"domain_scores_codex":[0.9988128,0.0001585723,0.00006632246,0.0004155148,0.0003577312,0.0001891049],"domain_scores_gemma":[0.9983664,0.0002698277,0.000158494,0.0003897366,0.0005029085,0.0003126008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002400995,0.0003383211,0.02076564,0.003091827,0.0004703993,0.0005749882,0.0003019489,0.008803463,0.1507545,0.009751991,0.6839067,0.1188392],"study_design_scores_gemma":[0.0007982628,0.0009114544,0.04899644,0.0006322697,0.0002651717,0.00122787,0.0002027096,0.05342843,0.1202382,0.0148807,0.7581404,0.0002779758],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.06215158,0.004525808,0.0631838,0.001757186,0.001127671,0.0008128228,0.7636803,0.05752911,0.04523178],"genre_scores_gemma":[0.0606938,0.00162235,0.05199281,0.001233483,0.0002408694,0.001212976,0.8638909,0.007501663,0.01161119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02061068,"threshold_uncertainty_score":0.06894964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02119131351393815,"score_gpt":0.2408419296338828,"score_spread":0.2196506161199447,"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."}}