{"id":"W2605682686","doi":"10.21769/bioprotoc.2222","title":"Efficient Generation of Multi-gene Knockout Cell Lines and Patient-derived Xenografts Using Multi-colored Lenti-CRISPR-Cas9","year":2017,"lang":"en","type":"article","venue":"BIO-PROTOCOL","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Canadian Institutes of Health Research; Universität Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Gene knockout; CRISPR; Cell sorting; Cas9; Biology; Computational biology; Transduction (biophysics); Cell culture; Gene knockin; Genome editing; Gene; Cell biology; Cell; Genetics","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":[0.00009566489,0.0002320741,0.0001999375,0.00005661031,0.0002512601,0.00006605248,0.0001918309,0.0001799733,0.00000912972],"category_scores_gemma":[0.00006571854,0.000218302,0.00009286252,0.00003454109,0.0001123714,0.000004600023,0.000236116,0.0000626219,0.000002072358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001225441,"about_ca_system_score_gemma":0.0000446708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006759329,"about_ca_topic_score_gemma":0.00003920033,"domain_scores_codex":[0.9988298,0.00003496817,0.000343274,0.0004072075,0.0001265867,0.0002581019],"domain_scores_gemma":[0.9989393,0.000003939857,0.0002493843,0.0005556052,0.000151856,0.00009991493],"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":[0.00006986297,0.0002001006,0.001184459,0.0001025077,0.00002211981,0.000002183076,0.00009875507,0.003395024,0.992316,4.363266e-7,0.00005569185,0.002552868],"study_design_scores_gemma":[0.002692304,0.0002396552,0.004337444,0.00002458266,0.00001958336,0.000004996785,0.00003914057,0.04303433,0.9464312,2.100999e-7,0.002933059,0.0002435435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.888658,0.00007826851,0.01673885,0.00001114227,0.0001833803,0.09426861,0.0000290169,0.00001403655,0.00001871131],"genre_scores_gemma":[0.9010454,0.000004431254,0.01959469,0.00002086098,0.0002093186,0.07900474,0.00002363357,0.00003681986,0.00006010061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04588484,"threshold_uncertainty_score":0.8902097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04573708340868193,"score_gpt":0.358507299095469,"score_spread":0.312770215686787,"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."}}