{"id":"W3196896120","doi":"10.1038/s41596-021-00596-0","title":"Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline","year":2021,"lang":"en","type":"article","venue":"Nature Protocols","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute; National Institutes of Health; Ontario Institute for Regenerative Medicine; National Science Foundation; U.S. Department of Health and Human Services; Government of Canada; Canadian Institutes of Health Research; Genome Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"CRISPR; Computer science; Guide RNA; Pipeline (software); Computational biology; Protocol (science); R package; Cas9; Data mining; Biology; Gene; Genetics; Programming language; Medicine","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":[0.000153185,0.0001161963,0.0001953365,0.00004524118,0.00004714738,0.00001771825,0.0001476575,0.0001814568,0.00001606821],"category_scores_gemma":[0.00007839822,0.00007897137,0.0001238616,0.0004911755,0.00003715142,0.000001654108,0.00008058579,0.0002292151,4.215834e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005159331,"about_ca_system_score_gemma":0.00007224626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000828378,"about_ca_topic_score_gemma":0.0001096019,"domain_scores_codex":[0.9992352,0.00003300222,0.0001514101,0.0002420064,0.0001867556,0.0001516368],"domain_scores_gemma":[0.9991964,0.00001530683,0.00006398124,0.0004439785,0.0002403636,0.00003994793],"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":[0.0006783805,0.0003125987,0.04802281,0.0002031255,0.002404694,0.00004035763,0.0001475908,0.01230101,0.9245379,0.001700124,0.003866243,0.00578517],"study_design_scores_gemma":[0.0009121264,0.0001887491,0.01053696,0.00004014371,0.0003129752,0.000007707288,0.00006660659,0.0003860714,0.928643,0.0000175707,0.05871006,0.000177957],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6901295,0.006790778,0.2063298,0.002008475,0.0008491946,0.08139929,0.00023992,0.000126916,0.01212613],"genre_scores_gemma":[0.9940547,0.000008077547,0.001077556,0.0001301778,0.0002392099,0.004158609,0.0000679215,0.00001631357,0.0002474052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3039252,"threshold_uncertainty_score":0.3220359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006856609575261395,"score_gpt":0.3312340295636992,"score_spread":0.3243774199884378,"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."}}