{"id":"W4386587299","doi":"10.1101/2023.09.08.23295253","title":"Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute","funders":"National Health and Medical Research Council; Medical Research Council; State Government of Victoria; American Heart Association","keywords":"Computational biology; Computer science; Genome editing; Pipeline (software); CRISPR; Phenotype; Base (topology); Biology; Genetics; Gene","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":[0.006296811,0.0009995855,0.001039395,0.001038642,0.0003022639,0.001305609,0.0009933099,0.0008113041,0.002087993],"category_scores_gemma":[0.01336365,0.0004008944,0.001106704,0.0006319823,0.0007309661,0.0007915224,0.001150964,0.001513301,0.0003179786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007271511,"about_ca_system_score_gemma":0.001121869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005285873,"about_ca_topic_score_gemma":0.005497199,"domain_scores_codex":[0.9978341,0.0009199454,0.0001173618,0.000575296,0.0004446093,0.000108564],"domain_scores_gemma":[0.9918927,0.006047474,0.000688011,0.000804981,0.000397071,0.0001697628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000579696,0.0001517344,0.03580023,0.0002281924,0.0005976111,0.000301396,0.0001043853,0.8536365,0.02222731,0.01224164,0.0016709,0.07246053],"study_design_scores_gemma":[0.00002930398,0.0000787397,0.004661193,0.00001750223,0.0000881752,0.0001001278,0.00001148769,0.971001,0.007016364,0.01588501,0.001081336,0.00002976295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07966086,0.0002349701,0.9153338,0.0002327623,0.00002804734,0.00006301401,0.001239414,0.002137376,0.001069765],"genre_scores_gemma":[0.8043879,0.0002069632,0.1907378,0.0002233403,0.00003477631,0.0001513154,0.002118943,0.0004991774,0.001639753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006296811,"threshold_uncertainty_score":0.03330112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03321093573455705,"score_gpt":0.3082631414409375,"score_spread":0.2750522057063804,"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."}}