{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002377167,0.001281551,0.00114754,0.001318886,0.0005755775,0.001574632,0.002893483,0.001581084,0.005641377],"category_scores_gemma":[0.002670432,0.001353165,0.001219274,0.000786747,0.0005606993,0.0008611894,0.002003781,0.003626351,0.006534051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007486056,"about_ca_system_score_gemma":0.0005418884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001058136,"about_ca_topic_score_gemma":0.00280997,"domain_scores_codex":[0.9967784,0.0003686539,0.0003248921,0.0006757831,0.001590148,0.0002622021],"domain_scores_gemma":[0.9980534,0.0005912987,0.0001433754,0.0007875266,0.0002797191,0.0001446552],"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":[0.0001114937,0.00009947335,0.0002661174,0.0001771004,0.00005472237,0.0001628236,0.00004322411,0.0008855839,0.9650847,0.0009428592,0.001644799,0.03052702],"study_design_scores_gemma":[0.00006687409,0.00008497552,0.001017112,0.00002343053,0.00004568304,0.00070269,0.00002561839,0.006668555,0.9608332,0.001148526,0.02929388,0.00008951739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07461725,0.0006626524,0.8858712,0.000537548,0.000536313,0.001104069,0.003554877,0.02513801,0.007978198],"genre_scores_gemma":[0.1495675,0.001118024,0.8170673,0.0006332429,0.00005882499,0.0009093731,0.006696526,0.004740381,0.01920897],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005641377,"threshold_uncertainty_score":0.01887226,"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."}}