{"id":"W4212840941","doi":"10.1186/s12864-022-08339-5","title":"Genion, an accurate tool to detect gene fusion from long transcriptomics reads","year":2022,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC","keywords":"Fusion gene; Computational biology; Gene; Biology; DNA microarray; Transcriptome; Alternative splicing; Fusion transcript; DNA sequencing; Gene prediction; Genetics; Computer science; Gene expression; Genome; Exon","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":[0.003580172,0.002391655,0.001427168,0.002724629,0.0007452705,0.002021784,0.002147456,0.001796022,0.008788321],"category_scores_gemma":[0.008484168,0.001065691,0.00171332,0.001507793,0.0008884604,0.001599253,0.002177994,0.002012913,0.003676561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128992,"about_ca_system_score_gemma":0.001409297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001668287,"about_ca_topic_score_gemma":0.00273418,"domain_scores_codex":[0.9975617,0.0003708187,0.0002058024,0.0008232603,0.0008975289,0.0001408583],"domain_scores_gemma":[0.9963321,0.002175611,0.0005521277,0.0003687146,0.0004368536,0.0001345572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004911997,0.0003835676,0.02444397,0.005600904,0.002218885,0.00291422,0.001794144,0.04763284,0.2811377,0.008867227,0.1110879,0.5090067],"study_design_scores_gemma":[0.000691027,0.0007017846,0.01765171,0.0003595512,0.0004083522,0.002601695,0.0003711291,0.5549194,0.2979359,0.01516363,0.1086306,0.0005652581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02624981,0.001329167,0.6160748,0.0003466283,0.0003406789,0.000359242,0.0130405,0.3402164,0.002042809],"genre_scores_gemma":[0.09856877,0.0006701602,0.8563453,0.001072441,0.00009461102,0.0009686709,0.02016925,0.0184757,0.003635124],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008788321,"threshold_uncertainty_score":0.02939987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431247381924409,"score_gpt":0.2295565165352446,"score_spread":0.2152440427160005,"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."}}