{"id":"W2413076967","doi":"10.1021/acschembio.5b00518","title":"<i>In Vitro</i> Screening and <i>in Silico</i> Modeling of RNA-Based Gene Expression Control","year":2015,"lang":"en","type":"letter","venue":"ACS Chemical Biology","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Complementary and Integrative Health; Bill and Melinda Gates Foundation; Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; National Institutes of Health; National Science Foundation","keywords":"In silico; Aptamer; RNA; Computational biology; Gene expression; Biology; Gene; In vitro; Genetics","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":[0.001158784,0.0004834167,0.0004357944,0.0002568778,0.0007867834,0.001356184,0.0007862636,0.005650792,0.004446011],"category_scores_gemma":[0.003191847,0.0003318122,0.0003970486,0.0002141159,0.0013327,0.0008399336,0.000420247,0.004461348,0.004026628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002147776,"about_ca_system_score_gemma":0.0005117579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005674128,"about_ca_topic_score_gemma":0.000806585,"domain_scores_codex":[0.9988668,0.0003911552,0.00006054331,0.0001094493,0.0004621334,0.0001099653],"domain_scores_gemma":[0.9991482,0.0004605824,0.00008717542,0.0000879996,0.0001580233,0.00005795212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005530369,0.0003088971,0.001603774,0.0006947392,0.00004476424,0.00637996,0.0002733041,0.004977648,0.1214874,0.04603321,0.7161962,0.1014472],"study_design_scores_gemma":[0.000163931,0.0004625557,0.0006854886,0.00009938719,0.00003171747,0.005653183,0.0001540612,0.03006045,0.1159421,0.01946084,0.8272141,0.00007232262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03029386,0.02298653,0.05000123,0.7865466,0.03575996,0.0002864412,0.0007228033,0.001135648,0.07226694],"genre_scores_gemma":[0.4256725,0.03540647,0.06542487,0.3378159,0.04076013,0.001204992,0.0008868031,0.0002821537,0.09254619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005650792,"threshold_uncertainty_score":0.01558328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130768521040768,"score_gpt":0.2626222449457632,"score_spread":0.2495453928416864,"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."}}