{"id":"W3201187849","doi":"10.1101/2021.09.22.461415","title":"Peptide fusion improves prime editing efficiency","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"","keywords":"Prime (order theory); Genome editing; Computer science; Computational biology; Peptide; Fusion; Biology; Combinatorial chemistry; Chemistry; Genome; Biochemistry; Gene; Mathematics; Combinatorics","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":[0.001382162,0.0005428076,0.0007965644,0.0003460208,0.0002829118,0.0012375,0.0004240161,0.0006886891,0.003128667],"category_scores_gemma":[0.001399679,0.0003466558,0.0003393382,0.0003638805,0.0004575806,0.000674815,0.000864003,0.001818997,0.001204562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003954726,"about_ca_system_score_gemma":0.0002494217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003708309,"about_ca_topic_score_gemma":0.0003912009,"domain_scores_codex":[0.9986663,0.0002428635,0.0001537942,0.0003788118,0.0003877324,0.0001705094],"domain_scores_gemma":[0.9983912,0.0007575866,0.0002302754,0.0002376506,0.0001797558,0.0002035151],"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":[0.00006821143,0.0000180455,0.0001915907,0.00002463331,0.000008010439,0.00002815,0.00001363012,0.0001867982,0.9979528,0.00009932271,0.0000863325,0.001322443],"study_design_scores_gemma":[0.000007584898,0.0001084957,0.0008113422,0.000003144421,0.00001316566,0.0001793143,0.00000919953,0.001677253,0.9951416,0.00007304019,0.001967852,0.000007934273],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9362574,0.001400879,0.05502985,0.0002715,0.0001216743,0.00009203813,0.001119521,0.001558347,0.004148841],"genre_scores_gemma":[0.9766032,0.0003244177,0.01843356,0.0002051614,0.00001662483,0.00003878158,0.001171962,0.0003917681,0.002814524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003128667,"threshold_uncertainty_score":0.01046646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005907328310489834,"score_gpt":0.2335910919607953,"score_spread":0.2276837636503055,"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."}}