{"id":"W1556607074","doi":"10.1002/0471142905.hg1112s73","title":"High‐Throughput Multiplex Sequencing of miRNA","year":2012,"lang":"en","type":"article","venue":"Current Protocols in Human Genetics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Ragon Institute of MGH, MIT and Harvard; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Multiplex; microRNA; Illumina dye sequencing; DNA sequencing; Computational biology; Hum; Multiplexing; Adapter (computing); Biology; Deep sequencing; Computer science; Genetics; Gene; Genome; Telecommunications; Computer hardware","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001054695,0.0007053387,0.0007318091,0.0009526532,0.0004842401,0.001067088,0.0005856702,0.0005627359,0.00328584],"category_scores_gemma":[0.0009227418,0.0004995322,0.0006219023,0.0005471433,0.0002470769,0.000553769,0.0005894175,0.0007948542,0.002074993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003226207,"about_ca_system_score_gemma":0.0003789822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002667959,"about_ca_topic_score_gemma":0.000599104,"domain_scores_codex":[0.9989663,0.0001655181,0.00008046308,0.0003644802,0.0003430256,0.00008020575],"domain_scores_gemma":[0.9995097,0.0001417566,0.00006234641,0.0001013183,0.0001253149,0.00005954858],"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":[0.000175698,0.00004665717,0.0004042627,0.0001172332,0.00004616957,0.00006392958,0.00006344781,0.0006364602,0.9616529,0.0009720628,0.001278939,0.03454221],"study_design_scores_gemma":[0.00003814591,0.000271307,0.002157922,0.00002095741,0.00007090557,0.0002565749,0.00002859873,0.0142145,0.950876,0.0009687825,0.0310491,0.00004711326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2268235,0.003409704,0.7446357,0.000338072,0.0006072993,0.0009204884,0.006911953,0.006077026,0.01027619],"genre_scores_gemma":[0.282689,0.002489773,0.68518,0.0004510848,0.000243029,0.001950183,0.007043085,0.0006343726,0.01931941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00328584,"threshold_uncertainty_score":0.01099217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06738042257283317,"score_gpt":0.3706321881208551,"score_spread":0.3032517655480219,"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."}}