{"id":"W4413051598","doi":"10.1016/j.omtn.2025.102636","title":"Off-target effects in CRISPR-Cas genome editing for human therapeutics: Progress and challenges","year":2025,"lang":"en","type":"article","venue":"Molecular Therapy — Nucleic Acids","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"HORIZON EUROPE Framework Programme; Consejería de Salud y Consumo, Junta de Andalucía; Junta de Andalucía; Novo Nordisk Fonden; Danmarks Frie Forskningsfond; Novo Nordisk; Deutsche Forschungsgemeinschaft; European Cooperation in Science and Technology; HORIZON EUROPE European Institute of Innovation and Technology; European Commission; Fundación Mutua Madrileña","keywords":"CRISPR; Genome editing; Computational biology; Biology; Human genome; Genome; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002194594,0.0002517529,0.0002357299,0.0001081013,0.00009220908,0.00003919043,0.0002048611,0.0001977291,0.000003055633],"category_scores_gemma":[0.00001700108,0.0002538392,0.000101347,0.00008958321,0.00008021856,0.000004180637,0.00008476578,0.0001070946,7.16638e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000017102,"about_ca_system_score_gemma":0.00002156089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003601888,"about_ca_topic_score_gemma":0.000008925785,"domain_scores_codex":[0.9988052,0.00005657087,0.000217785,0.0004619252,0.00009420978,0.0003643418],"domain_scores_gemma":[0.9994903,0.00001798522,0.00004392777,0.0003453034,0.00005019562,0.00005229851],"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":[0.00007947668,0.00009314081,0.0014927,0.0002231209,0.0002006666,0.00001041136,0.0003102063,0.00008636469,0.9146904,0.001153486,0.00001724845,0.08164274],"study_design_scores_gemma":[0.00325856,0.0007989142,0.01933247,0.00009460526,0.00004020777,0.000008098254,0.0002511259,0.0003963506,0.8536994,0.002240172,0.1193798,0.0005003285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7840586,0.181417,0.03214787,0.0008359233,0.0001909091,0.0008719501,0.000007120103,0.00003876046,0.000431906],"genre_scores_gemma":[0.9911413,0.00491255,0.00288112,0.0005648855,0.0001489889,0.0001975957,0.00003984104,0.00005537415,0.00005838996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2070827,"threshold_uncertainty_score":0.9999914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117030939455642,"score_gpt":0.3065583232516798,"score_spread":0.2948552293061156,"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."}}