{"id":"W4386989389","doi":"10.1016/j.jmoldx.2023.09.002","title":"Clinical Implementation of MetaFusion for Accurate Cancer-Driving Fusion Detection from RNA Sequencing","year":2023,"lang":"en","type":"article","venue":"Journal of Molecular Diagnostics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network; SickKids Foundation; Hospital for Sick Children","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Hospital for Sick Children; Ontario Genomics; Genome Canada","keywords":"Computational biology; Fusion gene; DNA sequencing; Transcriptome; Workflow; Identification (biology); Biology; Deep sequencing; RNA-Seq; Computer science; Gene; Genetics; Genome; Database; Gene expression","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":[0.0006186278,0.0001383033,0.0002873985,0.0001153776,0.00006454826,0.00002459165,0.0001816632,0.0001477505,0.00001116786],"category_scores_gemma":[0.001219701,0.0001364237,0.0002934283,0.0001646477,0.00003577791,0.000008872987,0.000113963,0.0001226013,0.000001281027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005926045,"about_ca_system_score_gemma":0.0003107433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00010887,"about_ca_topic_score_gemma":0.0003328942,"domain_scores_codex":[0.9984881,0.00008035968,0.0008085601,0.0002025654,0.0002230995,0.0001973436],"domain_scores_gemma":[0.9980426,0.0003803028,0.0008039122,0.0001971739,0.0004730499,0.0001030067],"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":[0.00008803038,0.00003038697,0.008316523,0.0000209842,0.000139584,0.00001713863,0.00005754481,0.003687989,0.935322,0.0000178077,0.001125627,0.05117639],"study_design_scores_gemma":[0.001482149,0.000836983,0.01748543,0.00009747485,0.000289457,0.000006364007,0.0003218503,0.0008945306,0.9736452,0.000488075,0.004280362,0.0001721221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.936734,0.001485711,0.06019307,0.0001332265,0.001076808,0.0002248574,0.0001389097,0.000005349453,0.000008074227],"genre_scores_gemma":[0.9803807,0.01581679,0.00282241,0.0001568403,0.0006752316,0.00001539149,0.00009553978,0.00003228044,0.000004789332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05737066,"threshold_uncertainty_score":0.5563198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187548977444165,"score_gpt":0.3666355482784036,"score_spread":0.334760058503962,"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."}}