{"id":"W3159344430","doi":"10.1093/bioinformatics/btab249","title":"MetaFusion: a high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Computer science; Benchmarking; Data mining; Graph; Fusion; Cluster analysis; Precision and recall; Majority rule; Selection (genetic algorithm); Machine learning; Information retrieval; Artificial intelligence; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008896324,0.002964741,0.001757489,0.005289178,0.002266721,0.003956315,0.003097639,0.002040593,0.02111199],"category_scores_gemma":[0.02186409,0.001660251,0.002672115,0.002497568,0.0007177605,0.002932111,0.003954851,0.002270569,0.01286731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068294,"about_ca_system_score_gemma":0.002220058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002345992,"about_ca_topic_score_gemma":0.005770948,"domain_scores_codex":[0.9950017,0.0008098916,0.0003895561,0.001568418,0.001845635,0.0003848163],"domain_scores_gemma":[0.9914419,0.004962218,0.000841886,0.001372558,0.0009522902,0.0004292275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004173928,0.0003540928,0.06136286,0.004333518,0.001738991,0.001488346,0.001974982,0.01448276,0.2631528,0.01220403,0.2953029,0.3394307],"study_design_scores_gemma":[0.0005502776,0.0006195387,0.04268713,0.0008008169,0.0009375775,0.003292615,0.0007017947,0.2811192,0.4495684,0.02062584,0.1981084,0.0009883601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04425148,0.00146262,0.5046625,0.0006075851,0.0004815249,0.0005248226,0.05507886,0.3834448,0.009485912],"genre_scores_gemma":[0.1567833,0.0004548906,0.7070666,0.001489097,0.0002247297,0.001509239,0.07067277,0.05503375,0.006765532],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02111199,"threshold_uncertainty_score":0.07062668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01830368811237904,"score_gpt":0.2595859352805155,"score_spread":0.2412822471681365,"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."}}