{"id":"W4307668581","doi":"10.1101/2022.10.26.513620","title":"License to cut: Smart RNA guides for conditional control of CRISPR-Cas9","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science; Agence Nationale de la Recherche","keywords":"Cas9; CRISPR; Synthetic biology; Computational biology; RNA; Guide RNA; Computer science; Genome editing; Flexibility (engineering); Genome engineering; Biology; Genetics; Gene; Mathematics","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":[0.0007818874,0.0005741667,0.0003334997,0.0007137559,0.0003705414,0.0009050972,0.000919498,0.00111134,0.04022937],"category_scores_gemma":[0.001389209,0.0005973502,0.0003566174,0.0004224668,0.0006705119,0.0006331239,0.001089918,0.001863805,0.02225715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003903312,"about_ca_system_score_gemma":0.0006297379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004172565,"about_ca_topic_score_gemma":0.0006287951,"domain_scores_codex":[0.9993271,0.00006544502,0.00004464661,0.000123027,0.0003563731,0.00008352866],"domain_scores_gemma":[0.9992865,0.0002167003,0.00008067685,0.0002369341,0.00009580082,0.00008349708],"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":[0.0007266282,0.0001684501,0.001093705,0.0006657866,0.00006250899,0.001031972,0.000310326,0.004747669,0.6130096,0.08120217,0.1521696,0.1448117],"study_design_scores_gemma":[0.0001397657,0.0001475732,0.0009008764,0.00008708346,0.00001986594,0.0007462362,0.00003541172,0.01329299,0.4950353,0.009564122,0.4799564,0.00007442352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05275771,0.001203509,0.7611901,0.001781753,0.002531189,0.0006488899,0.01575832,0.0900413,0.07408729],"genre_scores_gemma":[0.3299468,0.002116362,0.3766435,0.002126003,0.0005210014,0.002005239,0.03788872,0.03656339,0.2121889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04022937,"threshold_uncertainty_score":0.1345807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010864173983298,"score_gpt":0.2686151210075643,"score_spread":0.2585064792677313,"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."}}