{"id":"W4379521490","doi":"10.21428/594757db.0ff990d1","title":"Navitas/Optimus: A Novel Computational Tool for enhanced CRISPR/Cas Genome Editing","year":2023,"lang":"en","type":"article","venue":"","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"CRISPR; Genome editing; Computer science; Cas9; Subgenomic mRNA; Sequence (biology); Process (computing); Computational biology; Genome; Guide RNA; Artificial intelligence; Machine learning; Gene; Biology; Genetics; Programming language","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":[0.0008626477,0.001042965,0.0009803888,0.0005998602,0.0004649566,0.0007941634,0.001558615,0.001072723,0.005146494],"category_scores_gemma":[0.002470469,0.0005763625,0.0009756911,0.0004044902,0.0004054878,0.0006185215,0.000878024,0.001217662,0.0006643612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006180742,"about_ca_system_score_gemma":0.0014522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006869869,"about_ca_topic_score_gemma":0.01177001,"domain_scores_codex":[0.9997727,0.00007337199,0.00001543167,0.00004598061,0.00006748613,0.00002513194],"domain_scores_gemma":[0.9992889,0.0005346214,0.00004419903,0.00003040955,0.0000631513,0.00003865371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003285605,0.0001166883,0.002275483,0.0002935663,0.0001972178,0.0001914961,0.00008196296,0.9184829,0.003967944,0.01184618,0.006536013,0.05568193],"study_design_scores_gemma":[0.0000262797,0.00002464903,0.00007388088,0.000008536435,0.00001282165,0.00001628765,0.000005699674,0.9955243,0.0008156398,0.001607149,0.001877743,0.000007136432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06622569,0.0007549735,0.8973519,0.0006254941,0.0002908232,0.0002324754,0.001990903,0.02000301,0.01252476],"genre_scores_gemma":[0.2795261,0.0005051331,0.7098439,0.0004629734,0.00007439616,0.0008303929,0.002061654,0.001759856,0.004935494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006869869,"threshold_uncertainty_score":0.01721674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176638951095757,"score_gpt":0.3190505601914406,"score_spread":0.3072841706804831,"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."}}