{"id":"W3118238647","doi":"10.1101/gr.257246.119","title":"Accurate and efficient detection of gene fusions from RNA sequencing data","year":2021,"lang":"en","type":"article","venue":"Genome Research","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":524,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Heidelberger Zentrum für Personalisierte Onkologie Deutsches Krebsforschungszentrum In Der Helmholtz-Gemeinschaft; Nationales Centrum für Tumorerkrankungen Heidelberg; Ontario Institute for Cancer Research; Government of Ontario; Deutsches Krebsforschungszentrum","keywords":"KRAS; Biology; Fusion gene; ROS1; Computational biology; Druggability; Gene; Cancer; Identification (biology); Cancer research; Bioinformatics; Mutation; Genetics; Adenocarcinoma","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003298565,0.00121375,0.0009222953,0.002787884,0.0006479686,0.001751184,0.0008376673,0.001026013,0.001258966],"category_scores_gemma":[0.00926011,0.0006396195,0.000871888,0.00146294,0.0004902745,0.001151883,0.001009384,0.00153735,0.001820755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005976668,"about_ca_system_score_gemma":0.001058582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001512538,"about_ca_topic_score_gemma":0.00367876,"domain_scores_codex":[0.9977591,0.0004919032,0.000168278,0.0007562136,0.0006847749,0.0001397067],"domain_scores_gemma":[0.9957699,0.0026229,0.0004945215,0.0004033021,0.000585425,0.0001238391],"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":[0.0008458012,0.0002352653,0.02179601,0.0006112329,0.0004603995,0.0004872533,0.000335757,0.04007957,0.5986369,0.002988442,0.008597013,0.3249263],"study_design_scores_gemma":[0.00009157632,0.0002191426,0.01362323,0.00005105304,0.0000862419,0.0006588027,0.0002016016,0.7468864,0.2184435,0.007354302,0.01228137,0.0001028345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1198829,0.001296387,0.8562514,0.0004113771,0.00009766682,0.0002623714,0.003431637,0.0169434,0.001422774],"genre_scores_gemma":[0.2327395,0.0005163419,0.7584484,0.0002019969,0.00005779448,0.0002611203,0.005806461,0.0006704625,0.00129796],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003298565,"threshold_uncertainty_score":0.01744467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.266035831030826,"score_gpt":0.449513237461834,"score_spread":0.183477406431008,"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."}}