{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002311944,0.0001736467,0.0001730264,0.00006632083,0.00004485615,0.00001215352,0.0002371186,0.0001228246,0.00003886947],"category_scores_gemma":[0.0000467803,0.0001850052,0.0000781428,0.00009745398,0.0001027855,0.000006951157,0.0001631266,0.00009843635,0.00000547014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006511025,"about_ca_system_score_gemma":0.0000739472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006481132,"about_ca_topic_score_gemma":0.000009694137,"domain_scores_codex":[0.998722,0.00008798025,0.0004340107,0.0002556204,0.0001706787,0.0003297665],"domain_scores_gemma":[0.999126,0.000006807152,0.0002001281,0.0004869775,0.00008739977,0.00009272396],"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":[0.0000248677,0.0002168361,0.05827754,0.0002468746,0.00001310343,2.535312e-7,0.0001154226,0.000469008,0.9372291,0.0002611073,0.0002872664,0.00285864],"study_design_scores_gemma":[0.001034567,0.0001323209,0.0568113,0.0001788599,0.00001305848,0.000002418978,0.0000226639,0.0001091347,0.9256127,0.0002347063,0.01557857,0.0002697444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816009,0.001228591,0.0009171529,0.000003680427,0.0001536599,0.01597337,0.00001916469,0.00001045902,0.00009306672],"genre_scores_gemma":[0.9876267,0.0000191268,0.00214574,0.000009692963,0.000424986,0.009611796,0.0001092853,0.00003000115,0.00002270834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0152913,"threshold_uncertainty_score":0.7544293,"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."}}