{"id":"W4392923527","doi":"10.1101/2024.03.17.585235","title":"CROPseq-multi: a universal solution for multiplexed perturbation in high-content pooled CRISPR screens","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Broad Institute; National Institutes of Health; National Science Foundation","keywords":"CRISPR; Decoding methods; Multiplexing; Computer science; Computational biology; Biology; Algorithm; Genetics; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003436535,0.0004893256,0.0003876332,0.0002615603,0.0000790358,0.0001029319,0.0003194059,0.0006816834,0.000005810689],"category_scores_gemma":[0.0002326252,0.0005532787,0.0002250237,0.0001969567,0.00006022155,0.000007069829,0.000486532,0.0004384535,0.00001037635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002083755,"about_ca_system_score_gemma":0.0003167355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002325438,"about_ca_topic_score_gemma":0.0000913687,"domain_scores_codex":[0.9977528,0.00006076965,0.0004542493,0.001035812,0.0001784041,0.0005179796],"domain_scores_gemma":[0.9986386,0.00001924334,0.0001431675,0.0007145257,0.0003285654,0.0001558625],"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":[0.0001111086,0.00009164539,0.0006741616,0.0003376085,0.0001569141,0.00001274075,0.00001211766,0.001706881,0.9962303,0.0002403543,0.0004182569,0.00000793111],"study_design_scores_gemma":[0.002111962,0.0001667184,0.0366328,0.000398278,0.0002060591,3.944228e-8,0.0000134746,0.0341019,0.9217603,0.000004694158,0.003609733,0.0009940864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8651055,0.003492196,0.1276309,0.000276416,0.001495111,0.001368165,0.0004705989,0.0001581088,0.000003081154],"genre_scores_gemma":[0.9759664,0.0001851915,0.02277202,0.00009560922,0.0004993283,0.0002754833,0.00001092169,0.0001388657,0.00005617078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.110861,"threshold_uncertainty_score":0.9996918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823416729352757,"score_gpt":0.2553809113003262,"score_spread":0.2371467440067986,"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."}}