{"id":"W4283070781","doi":"10.1038/s41467-022-31270-y","title":"Peptide fusion improves prime editing efficiency","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"Qatar Biomedical Research Institute, Hamad Bin Khalifa University; National Human Genome Research Institute; Fundação de Amparo à Pesquisa do Estado de São Paulo; American Heart Association; U.S. Department of Health and Human Services","keywords":"Prime (order theory); Computer science; Genome editing; Translation (biology); Computational biology; Peptide; Function (biology); Biology; Genome; Biochemistry; Cell biology; 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":[0.001201514,0.000528937,0.0007914237,0.0003501173,0.0003050925,0.00133685,0.0004016205,0.0007192615,0.003004837],"category_scores_gemma":[0.001692674,0.0003677089,0.0003690703,0.0003076973,0.0005179089,0.0007782154,0.0008831717,0.001767948,0.001091816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004406558,"about_ca_system_score_gemma":0.000332892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004171579,"about_ca_topic_score_gemma":0.0005999817,"domain_scores_codex":[0.9986474,0.0002363141,0.0001499954,0.0003878899,0.0004146223,0.0001637434],"domain_scores_gemma":[0.9983188,0.0008200233,0.0002393867,0.0002308324,0.0002030186,0.0001878658],"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":[0.00004669783,0.00001535764,0.000187851,0.00002782048,0.000005902547,0.00002908405,0.00001529071,0.0001867609,0.9974069,0.0001566232,0.00007627796,0.001845327],"study_design_scores_gemma":[0.000007803768,0.0001306369,0.001007976,0.000004345892,0.00001534575,0.0002248793,0.00001081035,0.002040981,0.9934666,0.0001225309,0.002958657,0.000009345626],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9193084,0.001628269,0.06835637,0.0003582445,0.0001392446,0.0001025359,0.0009783292,0.001807891,0.007320737],"genre_scores_gemma":[0.9699281,0.0005310019,0.02405168,0.0002491424,0.00001647402,0.00004828206,0.0009899533,0.0003909975,0.003794471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003004837,"threshold_uncertainty_score":0.0100522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006614344046970064,"score_gpt":0.3022269127878583,"score_spread":0.2956125687408882,"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."}}