{"id":"W2274502064","doi":"10.1016/b978-0-12-801185-0.00010-6","title":"Adapting CRISPR/Cas9 for Functional Genomics Screens","year":2014,"lang":"en","type":"article","venue":"Methods in enzymology on CD-ROM/Methods in enzymology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"CRISPR; Cas9; Genome editing; Computational biology; Biology; Functional genomics; Guide RNA; Genome; Genomics; Subgenomic mRNA; 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":[0.008803017,0.0006475307,0.001050247,0.0006328238,0.0001603296,0.00002417022,0.0006495235,0.001273255,0.0000823746],"category_scores_gemma":[0.005272869,0.0007100765,0.0003180561,0.0003975756,0.0003785139,0.000009877513,0.0003667834,0.0008615621,0.00001195549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001418096,"about_ca_system_score_gemma":0.0001317897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007877757,"about_ca_topic_score_gemma":0.0005322401,"domain_scores_codex":[0.992107,0.003794758,0.001146991,0.001511654,0.0001545256,0.001285107],"domain_scores_gemma":[0.9954839,0.00286147,0.0002773419,0.001041519,0.000143626,0.0001921778],"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":[0.0006591534,0.0002537433,0.003025239,0.00007082187,0.0001124802,0.00001932395,0.0001781677,0.0193147,0.8740599,0.006633725,0.001005462,0.09466735],"study_design_scores_gemma":[0.00850937,0.004828815,0.1268833,0.0001019166,0.0001656592,0.0008324265,0.000568845,0.01176498,0.5388734,0.02728616,0.2775478,0.002637199],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1451423,0.001033663,0.8490366,0.0003226736,0.002067233,0.0005846728,0.00002197002,0.00005400095,0.001736961],"genre_scores_gemma":[0.1277058,0.0001163521,0.8688319,0.001436499,0.000839176,0.0003519123,0.00010068,0.0001303709,0.0004873309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3351864,"threshold_uncertainty_score":0.999535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04653007453263237,"score_gpt":0.4289506058911761,"score_spread":0.3824205313585438,"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."}}