{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001393791,0.0001309622,0.0002227166,0.00009868314,0.0000443168,0.0000493883,0.0000908677,0.00007986729,0.000004382495],"category_scores_gemma":[0.0000180296,0.0001289858,0.00008646736,0.0001120027,0.00002802186,0.000002545679,0.00006407391,0.00006791435,0.000002642189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005678485,"about_ca_system_score_gemma":0.00003298339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001985457,"about_ca_topic_score_gemma":0.000006633813,"domain_scores_codex":[0.9991764,0.00003392706,0.0002608249,0.0002542418,0.0001136892,0.0001608548],"domain_scores_gemma":[0.999658,0.00001682054,0.00003170771,0.0001732434,0.00004446082,0.00007576151],"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":[0.000007212601,0.00001080241,0.000122439,0.002843742,0.00003706285,0.000001366415,0.0001103227,0.2825196,0.7142226,0.000006174696,0.00004414585,0.00007463379],"study_design_scores_gemma":[0.00009143312,0.00004161768,0.00002535805,0.0004618293,0.00006672022,0.000001751004,0.0001121424,0.4145841,0.5844858,0.000004591292,0.00002817919,0.0000964978],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5924163,0.005486094,0.4018142,0.000007774936,0.00009155241,0.0001455698,0.000005881275,0.00001319728,0.00001937986],"genre_scores_gemma":[0.9948675,0.00004735817,0.004900285,0.00001623199,0.00005602825,0.000007608225,0.00001348333,0.00002613138,0.00006537037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4024512,"threshold_uncertainty_score":0.5259889,"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."}}