{"id":"W4361958132","doi":"10.1158/1078-0432.c.6530231.v1","title":"Data from Uncovering Clinically Relevant Gene Fusions with Integrated Genomic and Transcriptomic Profiling of Metastatic Cancers","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia; Pancreas Centre (Canada); Canada's Michael Smith Genome Sciences Centre","funders":"Canadian Institutes of Health Research; Ipsen; BC Cancer Foundation; Vancouver Coastal Health Research Institute; Eisai; Amgen; Pfizer; Canada Research Chairs; AstraZeneca; Eli Lilly and Company","keywords":"Transcriptome; RNA-Seq; Gene; Fusion gene; Biology; Genetics; Cancer; Contig; Cancer research; Genome; RNA; Computational biology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007306179,0.0004007751,0.0004976955,0.001276814,0.0003493235,0.0007748366,0.0003208671,0.0004984696,0.001863139],"category_scores_gemma":[0.001686402,0.0002096189,0.0005695853,0.001376306,0.0002466824,0.0002439807,0.0005926868,0.0004672938,0.0006240636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005216924,"about_ca_system_score_gemma":0.0004820137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002614123,"about_ca_topic_score_gemma":0.005109245,"domain_scores_codex":[0.9993898,0.00006304959,0.00005058336,0.0002003597,0.0002184423,0.00007770245],"domain_scores_gemma":[0.9991659,0.0002377518,0.0001425309,0.0001079762,0.0002286415,0.000117124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001353576,0.000129155,0.4738574,0.0008247846,0.0005079679,0.001185214,0.0003994759,0.004505905,0.449187,0.0007261386,0.007894798,0.05942859],"study_design_scores_gemma":[0.0001024377,0.0004927112,0.8387051,0.0001895667,0.0008456172,0.004106802,0.000580592,0.01576886,0.1118167,0.001330166,0.02597714,0.00008430256],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9533204,0.002802818,0.008334999,0.0004362263,0.00004602448,0.00008364501,0.03087776,0.0004052403,0.003692827],"genre_scores_gemma":[0.9543188,0.001166034,0.009103116,0.0003777219,0.00003892394,0.000125328,0.03383449,0.0001063176,0.0009293637],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.002614123,"threshold_uncertainty_score":0.006232858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05810219976730494,"score_gpt":0.3101386266386115,"score_spread":0.2520364268713066,"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."}}