{"id":"W3137430074","doi":"10.1093/bib/bbab055","title":"RNA2HLA: HLA-based quality control of RNA-seq datasets","year":2021,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council Canada; Medical Research Council; National Institute for Health and Care Research","keywords":"Human leukocyte antigen; RNA; Typing; Computer science; RNA-Seq; Computational biology; Transcriptome; Sample (material); Data mining; Biology; Gene; Genetics; Antigen; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005919329,0.0001716573,0.0002847819,0.00009090145,0.0000457922,0.00003496476,0.0002883783,0.0002380419,0.00005239076],"category_scores_gemma":[0.0008439717,0.0001856724,0.0001217367,0.0002840495,0.0001126969,0.00001036488,0.0001735389,0.0002056709,0.00001179204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004394714,"about_ca_system_score_gemma":0.0005284456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000168921,"about_ca_topic_score_gemma":0.0001227403,"domain_scores_codex":[0.998248,0.0001015724,0.0006738744,0.0002326852,0.000386271,0.0003575366],"domain_scores_gemma":[0.9987096,0.00005158791,0.0002295345,0.0006993128,0.0002102424,0.00009969092],"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":[0.0003456232,0.0002948623,0.0001941097,0.0006130399,0.0001517024,0.000041851,0.0001465861,0.003177594,0.9705469,0.001546644,0.0100481,0.01289298],"study_design_scores_gemma":[0.003125179,0.0001480182,0.000360875,0.00009741065,0.00002040624,0.00002241486,0.0001046618,0.009038965,0.9462742,0.0001268856,0.04038638,0.000294574],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1123968,0.002551493,0.8735391,0.002396623,0.0004525828,0.001057553,0.002663492,0.000053378,0.004888969],"genre_scores_gemma":[0.9717172,0.0002769805,0.02048796,0.004816633,0.00007183549,0.00003720001,0.002398143,0.00004316076,0.0001509172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8593204,"threshold_uncertainty_score":0.7571501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557302214901706,"score_gpt":0.3009012243782008,"score_spread":0.2853282022291837,"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."}}