{"id":"W4403136517","doi":"10.3390/cells13191653","title":"Systematic Comparison of CRISPR and shRNA Screens to Identify Essential Genes Using a Graph-Based Unsupervised Learning Model","year":2024,"lang":"en","type":"article","venue":"Cells","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Cancer Agency; University of Saskatchewan","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Canadian Institutes of Health Research; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"CRISPR; Gene; Computational biology; Biology; Small hairpin RNA; Genetics; Bioinformatics; RNA","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":[0.003064712,0.0006392274,0.0006569079,0.001144767,0.0002645827,0.0005124369,0.0007064982,0.0005572734,0.0004113266],"category_scores_gemma":[0.005260643,0.0002335548,0.0008471293,0.0003914829,0.0005745399,0.0007000146,0.000468583,0.0006812662,0.0001225113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107915,"about_ca_system_score_gemma":0.0009550169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003106457,"about_ca_topic_score_gemma":0.006411909,"domain_scores_codex":[0.9989734,0.0004825376,0.00005958648,0.0002054615,0.0001972892,0.00008179822],"domain_scores_gemma":[0.995204,0.003712526,0.0002851982,0.0003243363,0.0003667281,0.0001072662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004282427,0.0003054433,0.01419138,0.0002175907,0.0003621153,0.0001048814,0.00008040789,0.8705609,0.06755815,0.006766973,0.0006228679,0.03880112],"study_design_scores_gemma":[0.00000926883,0.00007951989,0.002171057,0.000003190606,0.00001572712,0.00001626825,0.000008034204,0.9877837,0.008167581,0.00165279,0.00008162807,0.00001121144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5761867,0.000242815,0.4193503,0.0002690231,0.00001761362,0.0001639199,0.0004613108,0.001552574,0.001755791],"genre_scores_gemma":[0.9156322,0.000121962,0.08239373,0.00009602964,0.000006165413,0.0001364254,0.0008578584,0.0001304154,0.0006253835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003106457,"threshold_uncertainty_score":0.01620793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01742286251709892,"score_gpt":0.354842508265633,"score_spread":0.3374196457485341,"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."}}