{"id":"W4385403239","doi":"10.1101/2023.07.27.550902","title":"A multiplex, prime editing framework for identifying drug resistance variants at scale","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Damon Runyon Cancer Research Foundation; Massachusetts Institute of Technology; Natural Sciences and Engineering Research Council of Canada; Karolinska Institutet; Harvard University; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Multiplex; Genome editing; Genome; Computational biology; Biology; Genetics; CRISPR; Context (archaeology); Gene; Prime (order theory)","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":[0.0006907303,0.0005846804,0.0004927196,0.0001483642,0.0002968356,0.0001780616,0.0006491665,0.0007488763,0.00001032246],"category_scores_gemma":[0.0007753352,0.00071489,0.0002950057,0.0002422748,0.00008033109,0.000008151304,0.001076803,0.0004974949,0.00003753679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001439613,"about_ca_system_score_gemma":0.0002038846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001666151,"about_ca_topic_score_gemma":0.00004510137,"domain_scores_codex":[0.9968357,0.00006509743,0.0005779585,0.001420199,0.0003132787,0.0007877719],"domain_scores_gemma":[0.9975331,0.00008715134,0.0003474639,0.001456849,0.0003256935,0.0002497097],"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":[0.00006135272,0.00004154244,0.001677304,0.0007483558,0.0001770597,0.00001285721,0.00002082259,0.0003869323,0.9942017,0.00009234523,0.002578252,0.000001507971],"study_design_scores_gemma":[0.0005423903,0.00002143238,0.01914265,0.000896333,0.0001117288,2.270844e-8,0.000008584519,0.0005482482,0.966274,0.0000214185,0.01149823,0.0009349853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6430613,0.003505181,0.3461822,0.0002936723,0.004461557,0.001419819,0.0006513433,0.0004177795,0.000007159739],"genre_scores_gemma":[0.9027433,0.0002974701,0.09327552,0.00009011994,0.002492229,0.0005502382,0.000006856657,0.0003094482,0.0002348405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.259682,"threshold_uncertainty_score":0.9995302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755069517021162,"score_gpt":0.2825491019750334,"score_spread":0.2649984068048218,"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."}}