{"id":"W4393152568","doi":"10.62051/dpgwbz03","title":"Mitigating the Off-target Effects in CRISPR/Cas9-mediated Genetic Editing with Bioinformatic Technologies","year":2024,"lang":"en","type":"article","venue":"Transactions on Materials Biotechnology and Life Sciences","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"CRISPR; Genome editing; Cas9; Computational biology; Biology; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002842671,0.000151955,0.000135876,0.0002246458,0.0002195433,0.00007019175,0.00020127,0.000232942,0.000009616404],"category_scores_gemma":[0.00007421327,0.00009628225,0.00002134361,0.0004156191,0.0006013113,0.00001152802,0.00001619439,0.0001669347,0.000006462989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005824928,"about_ca_system_score_gemma":0.0000456025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001840985,"about_ca_topic_score_gemma":0.00003168281,"domain_scores_codex":[0.9991236,0.00003121517,0.0001986757,0.000291869,0.00009155598,0.0002630754],"domain_scores_gemma":[0.9997005,0.00006786102,0.00003235373,0.0001660711,0.000009666627,0.00002348549],"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":[0.00003802483,0.00003669101,0.0002505515,0.0003651751,0.00007521916,0.00001969987,0.0003078301,0.004290176,0.9558361,0.0004209218,0.00008943971,0.03827014],"study_design_scores_gemma":[0.0001817655,0.0004654168,0.0004531385,0.0001656313,0.00001731805,0.00006355467,0.001299266,0.002469025,0.9930554,0.000191676,0.001467916,0.00016986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325302,0.004393979,0.05912561,0.003066491,0.0003374143,0.0002774303,0.00001756732,0.000206201,0.00004512621],"genre_scores_gemma":[0.9946184,0.001143035,0.004024909,0.00009616272,0.00003350202,0.0000616025,0.000003669957,0.00000988103,0.00000888508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06208817,"threshold_uncertainty_score":0.3926276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004733174749347196,"score_gpt":0.2494699234177301,"score_spread":0.2447367486683829,"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."}}