{"id":"W2992956341","doi":"10.1093/bioinformatics/btz902","title":"Fusion-Bloom: fusion detection in assembled transcriptomes","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre","funders":"National Human Genome Research Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Fusion; Computer science; Bloom; Bloom filter; Computational biology; Transcriptome; Artificial intelligence; Biology; Algorithm; Genetics; Gene expression; Gene","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":[0.004525679,0.002093527,0.001371934,0.003571045,0.00140894,0.002861081,0.001694697,0.001639963,0.007240286],"category_scores_gemma":[0.01156756,0.001190555,0.001968785,0.002292244,0.0008829049,0.00231445,0.003479295,0.001971599,0.005139492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001284975,"about_ca_system_score_gemma":0.001839885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001636263,"about_ca_topic_score_gemma":0.002814841,"domain_scores_codex":[0.9972663,0.0003779418,0.000249452,0.001156261,0.0007220135,0.000228102],"domain_scores_gemma":[0.996283,0.001901299,0.0005891616,0.000598924,0.0004155405,0.0002119996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005344401,0.0004171568,0.08138907,0.004266252,0.001898575,0.002325553,0.002025695,0.03561351,0.3584667,0.02060185,0.1738558,0.3137954],"study_design_scores_gemma":[0.0004742963,0.0006512054,0.03908332,0.0004487589,0.000482583,0.002868001,0.0005763217,0.4402389,0.3430272,0.04504599,0.1265791,0.0005243597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1227673,0.001525586,0.5514677,0.0007298513,0.0005900345,0.0004391568,0.07169244,0.2455155,0.005272526],"genre_scores_gemma":[0.2261652,0.0006117146,0.6227125,0.0005883545,0.0001528901,0.0006863683,0.1274909,0.01878027,0.002811847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007240286,"threshold_uncertainty_score":0.02422118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00669476493309633,"score_gpt":0.2164349260170814,"score_spread":0.209740161083985,"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."}}