{"id":"W4317936271","doi":"10.1002/cpz1.646","title":"Identifying Genetic Regulators of Protein‐Glycan Interactions with Genome‐Wide CRISPR Screening","year":2023,"lang":"en","type":"article","venue":"Current Protocols","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Glycan; Biology; Computational biology; Glycosylation; CRISPR; Immune system; Gene; Genetics; Glycoprotein","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":[0.0001314026,0.0001798205,0.000167507,0.0001317916,0.00008024671,0.0000355564,0.0002166519,0.00005186184,0.00002220285],"category_scores_gemma":[0.0000511327,0.0001680771,0.00008791926,0.0003098824,0.00005518542,0.000007248501,0.0001408033,0.0001256174,0.00001156686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001156083,"about_ca_system_score_gemma":0.00005334512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006139514,"about_ca_topic_score_gemma":0.00001196586,"domain_scores_codex":[0.9988402,0.00003231539,0.0002983583,0.0003448275,0.0001894984,0.000294855],"domain_scores_gemma":[0.9992568,0.000008838233,0.0001184796,0.0004243458,0.00009862189,0.00009288605],"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":[0.0001617641,0.0001017802,0.020529,0.0007602579,0.0001213904,0.000006906655,0.0002772278,0.005305002,0.9506623,0.00004212594,0.0007181613,0.02131406],"study_design_scores_gemma":[0.001173013,0.0004624383,0.09644161,0.0008201342,0.00003670302,0.00001908152,0.0002837425,0.0006347395,0.7933464,0.00007454403,0.1061865,0.0005210862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.789598,0.0003463137,0.1791569,0.0000481323,0.0001337792,0.03045371,0.00002298076,0.00009660191,0.0001436332],"genre_scores_gemma":[0.957739,0.00001711592,0.004561879,0.000008572506,0.0002186815,0.03696152,0.00008518059,0.00005576681,0.0003523045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.174595,"threshold_uncertainty_score":0.6853986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03587425920216512,"score_gpt":0.3830464443491076,"score_spread":0.3471721851469424,"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."}}