{"id":"W4381380002","doi":"10.7554/elife.84792.2","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; Interference (communication); Computer science; Biology; Genetics; Telecommunications; Gene; RNA","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":[0.0004340655,0.0004791163,0.0003303498,0.0002751765,0.00025044,0.0005163584,0.000415916,0.0003465335,0.001977144],"category_scores_gemma":[0.0002467064,0.0002836031,0.0002885773,0.0002392375,0.0003627564,0.0002993291,0.0005071776,0.0008886453,0.001560868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003083533,"about_ca_system_score_gemma":0.0002903997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002015584,"about_ca_topic_score_gemma":0.0003491179,"domain_scores_codex":[0.999468,0.00005316777,0.00006594164,0.0001222259,0.0002376294,0.00005316929],"domain_scores_gemma":[0.9998179,0.0000339244,0.00004736517,0.00005022334,0.00002782718,0.00002291295],"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":[0.0000339284,0.0000110889,0.00005368239,0.00004278203,0.000004201571,0.00002403962,0.000009280144,0.0001626742,0.9962084,0.0007817103,0.0001711097,0.002497085],"study_design_scores_gemma":[0.000009768296,0.00005948457,0.0002246042,0.000005015343,0.000006059965,0.0001019135,0.000003664659,0.001074068,0.9910575,0.0001554036,0.007297688,0.000004862196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5829765,0.003180252,0.3757239,0.0003659903,0.0004385517,0.00062637,0.004850673,0.007028589,0.02480916],"genre_scores_gemma":[0.8458641,0.001689747,0.1259088,0.0002250126,0.00005989711,0.000380842,0.005708069,0.0008635649,0.0193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001977144,"threshold_uncertainty_score":0.006614208,"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."}}