{"id":"W2142565931","doi":"10.1101/gr.121715.111","title":"Barcoding bias in high-throughput multiplex sequencing of miRNA","year":2011,"lang":"en","type":"article","venue":"Genome Research","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health; National Human Genome Research Institute; United States-Israel Binational Science Foundation; Fondation Leducq","keywords":"Biology; Barcode; microRNA; Multiplex; Computational biology; DNA sequencing; Deep sequencing; Adapter (computing); Genetics; Gene expression profiling; DNA microarray; DNA; Gene; Genome; Gene expression; Computer science","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":[0.001017849,0.00008703168,0.0001199452,0.000195995,0.00005026553,0.000008890611,0.0002500493,0.0001111024,0.0001289839],"category_scores_gemma":[0.0002262088,0.00008908009,0.00004937165,0.0002738054,0.0001382439,0.000005077133,0.0002298485,0.0001334104,0.00002658574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000895395,"about_ca_system_score_gemma":0.0001846043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006594655,"about_ca_topic_score_gemma":0.000111314,"domain_scores_codex":[0.9986269,0.000206761,0.0002388833,0.0003053067,0.0002497207,0.0003723959],"domain_scores_gemma":[0.9992777,0.0000247047,0.00005012225,0.0003942922,0.0001749557,0.00007823358],"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":[0.0000663443,0.00004077097,0.008572944,0.00005524251,0.00001728733,0.000008780036,0.0003110938,0.00005881857,0.9900167,0.0001443245,0.00004459528,0.0006631195],"study_design_scores_gemma":[0.0005521196,0.0001404825,0.1314855,0.00003667912,0.000003424067,0.000005075598,0.0002914595,0.0001152195,0.8654791,0.0004610695,0.001286386,0.0001435596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969308,0.0008903649,0.000276903,0.00001975242,0.00002860375,0.0002411815,0.0000139617,0.000005237298,0.001593213],"genre_scores_gemma":[0.9968903,0.0001425505,0.002612782,0.00000833917,0.00008269738,0.00002030886,0.00005003066,0.00002028755,0.0001726682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1245376,"threshold_uncertainty_score":0.3632581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2164933785269231,"score_gpt":0.359315252451359,"score_spread":0.1428218739244359,"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."}}