{"id":"W4380485664","doi":"10.3390/cancers15123158","title":"Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach","year":2023,"lang":"en","type":"article","venue":"Cancers","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; Research Institute in Oncology and Hematology; Children's Hospital Research Institute of Manitoba; University of Manitoba","funders":"Fundação para a Ciência e a Tecnologia; Rede de Química e Tecnologia; Canadian Institutes of Health Research; Laboratório Associado para a Química Verde; European Commission","keywords":"Glioblastoma; Gene; microRNA; Temozolomide; Disease; Cancer research; Medicine; Computational biology; Bioinformatics; Biology; Internal medicine; 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":[0.00004275081,0.0001087697,0.0002186562,0.00005977273,0.00004966294,0.00001185304,0.0001214246,0.00007264311,0.000001131504],"category_scores_gemma":[0.00001307875,0.00009395138,0.00008322292,0.0001977,0.00006380623,0.00000319576,0.00008408993,0.00002092738,4.197865e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005824261,"about_ca_system_score_gemma":0.00007369358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006760547,"about_ca_topic_score_gemma":0.0002675065,"domain_scores_codex":[0.9993891,0.00001050302,0.0001774137,0.0002368558,0.00003693333,0.0001492699],"domain_scores_gemma":[0.9994555,0.00002780491,0.0001050164,0.000312943,0.0000537846,0.00004498462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001007646,0.0002376196,0.009896176,0.0005181469,0.01159379,0.000002648618,0.006402748,0.5153785,0.1955016,0.0002409625,0.008071791,0.2511484],"study_design_scores_gemma":[0.001765202,0.0006972476,0.0008959017,0.00001385452,0.0004944643,9.050798e-7,0.002129521,0.9477338,0.03695508,0.00001339932,0.009047609,0.0002530207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9137022,0.0008045169,0.08424229,0.00001997747,0.0000749583,0.0004355327,0.0006467474,0.00001183051,0.00006197662],"genre_scores_gemma":[0.9815844,0.001958431,0.0146686,0.00003993728,0.00007588509,0.0001612753,0.00143348,0.00001190212,0.00006606061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4323553,"threshold_uncertainty_score":0.3831226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04312370533409424,"score_gpt":0.2977176361499548,"score_spread":0.2545939308158606,"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."}}