{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002029778,0.001338613,0.0009484374,0.001272859,0.0004653734,0.001239495,0.0007717141,0.0003549933,0.004152265],"category_scores_gemma":[0.002621715,0.0003494246,0.0010719,0.001101778,0.0003579782,0.0004517369,0.0009049368,0.0006963896,0.001092085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00076356,"about_ca_system_score_gemma":0.0008009853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670096,"about_ca_topic_score_gemma":0.001901221,"domain_scores_codex":[0.9983966,0.0002697296,0.0001287856,0.0003138331,0.0007537999,0.0001373868],"domain_scores_gemma":[0.9986758,0.0006107416,0.0001835119,0.000205594,0.0002451145,0.00007934393],"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":[0.00300051,0.0007114863,0.04184441,0.001310661,0.0007376921,0.001243276,0.0002948077,0.1975895,0.5438489,0.01670269,0.01612151,0.1765946],"study_design_scores_gemma":[0.0001239917,0.001109279,0.03048205,0.00005779157,0.0002188163,0.0006189565,0.0001087122,0.6723251,0.279229,0.005070972,0.01052783,0.0001274535],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6472309,0.0003279082,0.3059133,0.000199744,0.00005801087,0.0005257081,0.01659303,0.01658172,0.0125696],"genre_scores_gemma":[0.7544163,0.0002627737,0.2174426,0.0001786394,0.0000159249,0.0008361746,0.02128039,0.00219732,0.003369781],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004152265,"threshold_uncertainty_score":0.01389074,"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."}}