{"id":"W3023817783","doi":"10.1101/2020.05.07.083444","title":"Automated design of CRISPR prime editors for thousands of human pathogenic variants","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Advanced Research Projects Agency; National Institutes of Health; York University; Sidney Kimmel Foundation; Canadian Institutes of Health Research; Melanoma Research Alliance","keywords":"CRISPR; Genome editing; Computational biology; Human genome; Computer science; Human disease; Genome; Gene; dbSNP; Cas9; Prime (order theory); Genetics; Biology; Genotype; Single-nucleotide polymorphism; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004205959,0.0004422455,0.0006210242,0.0001236245,0.00006038076,0.00002688601,0.0005324311,0.0006323683,0.000007262975],"category_scores_gemma":[0.0002131243,0.0004974831,0.0002543756,0.0001913359,0.0001036563,0.000004242804,0.0003784272,0.0002230607,0.000001641304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002991416,"about_ca_system_score_gemma":0.0004204942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009999271,"about_ca_topic_score_gemma":2.971665e-7,"domain_scores_codex":[0.9978906,0.00008601998,0.0006641564,0.0007569316,0.0002280276,0.0003742815],"domain_scores_gemma":[0.9979583,0.00002873735,0.0004496259,0.0009438167,0.0004495108,0.0001700223],"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":[0.00006643657,0.00009058758,0.0003313055,0.0006239103,0.0002738557,0.000003548599,0.000007913928,0.001592225,0.9953553,0.00003336429,0.001620331,0.000001193635],"study_design_scores_gemma":[0.0006272899,0.0003740565,0.008913468,0.0001553786,0.0001817564,1.462746e-8,0.000001958011,0.003543223,0.9851207,0.000002163431,0.0006088493,0.0004711194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5108696,0.002115381,0.481388,0.0000628885,0.002536838,0.001786995,0.0009218322,0.0003135523,0.000004848166],"genre_scores_gemma":[0.9808387,0.0001237197,0.0172788,0.00002349975,0.001450861,0.0001411139,0.000004365891,0.0001366438,0.000002296973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4699691,"threshold_uncertainty_score":0.9997477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539655937136461,"score_gpt":0.2746169704372924,"score_spread":0.2592204110659278,"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."}}