{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001549501,0.0006526448,0.0008628277,0.000493901,0.0003078502,0.0012464,0.0007379042,0.0008672295,0.001070384],"category_scores_gemma":[0.002009708,0.0002729045,0.0007811889,0.000441292,0.0007768735,0.0008949622,0.0006977238,0.001603298,0.0004407218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000542452,"about_ca_system_score_gemma":0.0006575058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003828191,"about_ca_topic_score_gemma":0.0006848808,"domain_scores_codex":[0.9988122,0.0003227629,0.00009596673,0.0001819209,0.0005068213,0.00008034686],"domain_scores_gemma":[0.9990158,0.000644755,0.0001498065,0.00006707138,0.00009787572,0.00002463134],"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":[0.0001900346,0.0001477916,0.001645527,0.003952533,0.0002927888,0.0007752655,0.0002049514,0.03181083,0.6602962,0.02489052,0.001687419,0.2741061],"study_design_scores_gemma":[0.00004423107,0.001163348,0.002423012,0.0004980173,0.0003076529,0.002485093,0.0001399924,0.06468107,0.8326263,0.02002718,0.07547182,0.0001322809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.110852,0.08063357,0.7858992,0.002317362,0.0005299795,0.0003128069,0.0002950286,0.001788398,0.01737164],"genre_scores_gemma":[0.601281,0.07760517,0.3126518,0.00139655,0.0001739595,0.0002663781,0.0004664851,0.0002676082,0.005891014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001549501,"threshold_uncertainty_score":0.008194625,"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."}}