{"id":"W4323042354","doi":"10.7554/elife.84792.1","title":"Precision RNAi using synthetic shRNAmir target sites","year":2023,"lang":"en","type":"preprint","venue":"","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advantage Forensics (Canada)","funders":"Österreichische Forschungsförderungsgesellschaft; European Commission; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"RNA interference; Computational biology; Synthetic biology; Loss function; Function (biology); Scalability; Computer science; Biology; Gene; Genetics; RNA; Phenotype","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004613107,0.0004780876,0.000331211,0.0002810345,0.0002617794,0.000529995,0.0004188606,0.0003530099,0.002012702],"category_scores_gemma":[0.0002609899,0.0002924312,0.0002994881,0.0002335713,0.0003758022,0.0003168967,0.0005143682,0.0009399994,0.001596823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003238932,"about_ca_system_score_gemma":0.0003037285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002193147,"about_ca_topic_score_gemma":0.0003815443,"domain_scores_codex":[0.9994475,0.00005499349,0.00006636619,0.0001247145,0.0002512931,0.00005507495],"domain_scores_gemma":[0.9998102,0.00003565918,0.00004822433,0.00005369469,0.00002849561,0.00002377231],"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":[0.0000344151,0.00001141696,0.00005494228,0.00004446897,0.000004330432,0.00002610753,0.00001014558,0.0001678765,0.9958475,0.0008940205,0.0001962508,0.002708602],"study_design_scores_gemma":[0.000009529474,0.00005726274,0.0002201581,0.000005146387,0.000005905174,0.0001043122,0.000003766014,0.001144229,0.9906427,0.0001625955,0.00763927,0.000005135033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5703241,0.003237041,0.3868814,0.000392146,0.0004642852,0.0006304521,0.004707858,0.00722501,0.0261376],"genre_scores_gemma":[0.8394455,0.00174856,0.1305283,0.0002243524,0.00006359658,0.0003754731,0.005623931,0.0009316386,0.02105868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002012702,"threshold_uncertainty_score":0.006733179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04725801866098579,"score_gpt":0.3069345401058146,"score_spread":0.2596765214448288,"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."}}