{"id":"W2154401446","doi":"10.1093/nar/gkv993","title":"Nuclear domain ‘knock-in’ screen for the evaluation and identification of small molecule enhancers of CRISPR-based genome editing","year":2015,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":290,"is_retracted":false,"has_abstract":true,"ca_institutions":"Beatrice Hunter Cancer Research Institute; Dalhousie University","funders":"Canadian Institutes of Health Research; Terry Fox Foundation; Beatrice Hunter Cancer Research Institute","keywords":"CRISPR; Biology; Genome editing; Cas9; Homology directed repair; Gene; Homologous recombination; Genome engineering; Genetics; Enhancer; Genome; Computational biology; Gene knockin; Endonuclease; Locus (genetics); DNA repair; Nucleotide excision repair; Transcription factor","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002697252,0.00006392971,0.00009171412,0.00008751704,0.0000498089,0.00001469327,0.000185121,0.00007746701,0.000007023335],"category_scores_gemma":[0.0004890774,0.00005657677,0.00003524315,0.0001459838,0.0001240531,0.000003141158,0.00007437827,0.00008162513,9.61651e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002764686,"about_ca_system_score_gemma":0.00009290647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005129293,"about_ca_topic_score_gemma":0.00003296743,"domain_scores_codex":[0.9989852,0.0000988781,0.0002247985,0.0001959712,0.0003111476,0.0001839904],"domain_scores_gemma":[0.9991274,0.00005229198,0.00005674801,0.000266067,0.0004497787,0.00004764773],"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":[0.0000998421,0.00002336487,0.0003734293,0.00005077973,0.00001540414,1.303854e-7,0.0002702099,0.002483605,0.9928638,0.00005895393,0.0001453189,0.0036151],"study_design_scores_gemma":[0.00138911,0.000435151,0.009855544,0.00002762419,0.00001924491,0.000001106391,0.001866568,0.05077531,0.9332526,0.0002422982,0.002029766,0.0001056449],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9630659,0.00101314,0.03488896,0.0002030634,0.00003836475,0.0005416953,0.00001634576,0.000002979901,0.0002296017],"genre_scores_gemma":[0.9979526,0.00004453772,0.001785271,0.00001067165,0.00008896686,0.00004465433,0.00003000764,0.00001768825,0.00002554237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05961123,"threshold_uncertainty_score":0.2307134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05882218591989418,"score_gpt":0.3966957467069421,"score_spread":0.3378735607870479,"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."}}