{"id":"W4381053620","doi":"10.1101/2023.06.16.545157","title":"A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"","keywords":"CRISPR; Scalability; Computational biology; Genetic screen; DNA damage; Cas9; DNA; Biology; Computer science; Genetics; Gene; Phenotype","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":[0.001148795,0.001027183,0.0011986,0.001145339,0.000459263,0.00120443,0.001209698,0.0008779701,0.006423289],"category_scores_gemma":[0.0008605497,0.0007907471,0.0006600039,0.0005191965,0.0004124325,0.0005314663,0.00147679,0.002008834,0.003991101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007510058,"about_ca_system_score_gemma":0.0007018847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007238204,"about_ca_topic_score_gemma":0.001861634,"domain_scores_codex":[0.9987836,0.0001084585,0.00009272152,0.0002558044,0.0006632641,0.00009607716],"domain_scores_gemma":[0.9995207,0.0001203315,0.00008590076,0.0001042616,0.00006689467,0.0001020304],"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":[0.00007572418,0.00006776527,0.00008785046,0.00008288248,0.00002773014,0.00009704389,0.00001320475,0.001029822,0.9894916,0.0005385195,0.001330044,0.007157855],"study_design_scores_gemma":[0.0000779145,0.0002346643,0.000525501,0.000014235,0.00003075341,0.0001900177,0.0000122461,0.0077002,0.9747138,0.0004384139,0.01602562,0.00003660912],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1802458,0.001949703,0.7417354,0.001032845,0.0004308035,0.002670339,0.02030126,0.04131857,0.01031522],"genre_scores_gemma":[0.4130603,0.00168737,0.5450515,0.0004464539,0.00008085909,0.003013754,0.01938307,0.002875775,0.0144009],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006423289,"threshold_uncertainty_score":0.02148807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752961963113157,"score_gpt":0.263212188044613,"score_spread":0.2456825684134814,"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."}}