{"id":"W4310841195","doi":"10.21203/rs.3.rs-2326674/v1","title":"A multi-omics analysis of glioma chemoresistance using a hybrid microphysiological model of glioblastoma","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação para a Ciência e a Tecnologia; BC Cancer Foundation; Research Manitoba","keywords":"Glioblastoma; Glioma; Computational biology; Cancer research; Medicine; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002716612,0.0004534637,0.00219497,0.0016977,0.0001709445,0.00002706367,0.001547189,0.0003685028,0.0005092385],"category_scores_gemma":[0.003638293,0.0003986555,0.001080744,0.002029191,0.0009262173,0.00003460638,0.004207964,0.001816163,0.000003489044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004373983,"about_ca_system_score_gemma":0.0004853634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008452778,"about_ca_topic_score_gemma":0.00002197913,"domain_scores_codex":[0.9943146,0.001048982,0.001374985,0.001009564,0.00145068,0.0008011888],"domain_scores_gemma":[0.9939438,0.002150075,0.0008294142,0.001899539,0.0009896137,0.0001876355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001546887,0.004026602,0.0008221501,0.01692438,0.004430234,0.0002253885,0.001264271,0.0245323,0.9167967,0.02897256,0.0003926315,0.00006592712],"study_design_scores_gemma":[0.0004940091,0.000277897,0.0003583123,0.0004013724,0.0008077915,0.000006849093,0.0003436804,0.8026806,0.05945528,0.1347553,0.000002529949,0.0004163061],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846413,0.0002177416,0.0112478,0.00003301181,0.00003383105,0.001079907,0.00249001,0.00006039396,0.0001960119],"genre_scores_gemma":[0.8953589,0.00003366341,0.1041528,0.000005256758,0.00002696603,0.0001573307,0.0001047128,0.00006287327,0.00009759954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8573414,"threshold_uncertainty_score":0.9998465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1972813493077599,"score_gpt":0.4418333035426733,"score_spread":0.2445519542349133,"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."}}