{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006353789,0.0004552586,0.0004422282,0.0004262643,0.0002690948,0.0005465384,0.0004931511,0.0004406073,0.002351818],"category_scores_gemma":[0.0001947652,0.0003282765,0.0004099487,0.000261211,0.0002084877,0.0003209931,0.0005029325,0.001184519,0.001287422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002604304,"about_ca_system_score_gemma":0.0004142314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002813739,"about_ca_topic_score_gemma":0.0007027395,"domain_scores_codex":[0.999616,0.00005581439,0.00005819442,0.00007604974,0.0001438283,0.00005015548],"domain_scores_gemma":[0.9998265,0.00003183485,0.00003350213,0.00005358988,0.00002111943,0.00003348175],"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":[0.00005882446,0.00004943677,0.0002088537,0.00005346684,0.00001160194,0.0001249373,0.0000268598,0.0002025819,0.994002,0.0006826012,0.0005299778,0.004048842],"study_design_scores_gemma":[0.00002263148,0.0001184819,0.0006709141,0.000009051688,0.0000225584,0.0007504453,0.00001374192,0.001519655,0.9847735,0.0001307734,0.01196027,0.000008058142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.629681,0.00310047,0.3391986,0.0007510704,0.0003929186,0.00214906,0.008895341,0.004063016,0.01176866],"genre_scores_gemma":[0.7931193,0.002973853,0.1765228,0.0002636861,0.00002797046,0.001609356,0.007847227,0.0005847494,0.01705114],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002351818,"threshold_uncertainty_score":0.007867634,"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."}}