{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001312906,0.0006506668,0.0007358161,0.0008128119,0.0002625631,0.001151851,0.001034907,0.0005442757,0.002069924],"category_scores_gemma":[0.001062174,0.0005427858,0.0004121927,0.0003412019,0.0009537044,0.0008100596,0.001238325,0.00119154,0.0007804352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006238918,"about_ca_system_score_gemma":0.0003948486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004978558,"about_ca_topic_score_gemma":0.0008882982,"domain_scores_codex":[0.9991263,0.0001113946,0.00004512395,0.0003370207,0.0003078611,0.00007229377],"domain_scores_gemma":[0.9993112,0.000205358,0.0001443299,0.0001645246,0.00007910042,0.00009538375],"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":[0.0002354966,0.00009119233,0.0006586017,0.00008523365,0.00003363681,0.0001872547,0.00004686973,0.005651084,0.9514654,0.01103848,0.0006888409,0.02981783],"study_design_scores_gemma":[0.00005680249,0.0003136375,0.0008015406,0.00001378726,0.00004058957,0.0004678953,0.00002664989,0.05949269,0.9165549,0.00842245,0.01374147,0.00006771217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08018617,0.0005827409,0.9085808,0.0001894122,0.00008180334,0.0002239212,0.0007354557,0.006863142,0.002556585],"genre_scores_gemma":[0.4686949,0.0005939233,0.5236744,0.0001944041,0.00004693121,0.000408457,0.0008171676,0.0004278993,0.005141946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002069924,"threshold_uncertainty_score":0.006943405,"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."}}