{"id":"W3029194094","doi":"10.1186/s13059-020-02046-8","title":"MAUDE: inferring expression changes in sorting-based CRISPR screens","year":2020,"lang":"en","type":"article","venue":"Genome biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Klarman Cell Observatory, Broad Institute; National Institutes of Health; National Human Genome Research Institute; Howard Hughes Medical Institute","keywords":"CRISPR; Sorting; Biology; Leverage (statistics); Computational biology; Expression (computer science); Gene expression; Gene; Computer science; Genetics; Artificial intelligence; Algorithm; Programming language","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":[0.006108657,0.001481639,0.001643974,0.002420077,0.0006718397,0.002300111,0.00304419,0.001819505,0.002707613],"category_scores_gemma":[0.02166138,0.001475535,0.002689464,0.001495902,0.001030736,0.001471204,0.001750597,0.002950955,0.001214342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001438141,"about_ca_system_score_gemma":0.001319525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003089157,"about_ca_topic_score_gemma":0.0077868,"domain_scores_codex":[0.9968257,0.001094631,0.0001834079,0.001046403,0.0007006594,0.0001493377],"domain_scores_gemma":[0.9895433,0.008195589,0.0006080764,0.001137904,0.000374993,0.0001400498],"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":[0.001119104,0.0003799666,0.07135212,0.001791065,0.001678026,0.0007938574,0.0007675498,0.5513902,0.1582928,0.05887537,0.01541159,0.1381484],"study_design_scores_gemma":[0.00009795723,0.0001668564,0.007078139,0.00008516973,0.0002167872,0.0003448108,0.00008450765,0.8590817,0.06609356,0.05356747,0.0130648,0.0001182989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03402119,0.0003146036,0.9396625,0.000155739,0.00003454843,0.00009385965,0.005899552,0.01900323,0.0008146767],"genre_scores_gemma":[0.247369,0.0004852757,0.7340682,0.0005788387,0.00003491623,0.0006949345,0.01053971,0.004912641,0.001316477],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006108657,"threshold_uncertainty_score":0.03230608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01773218496093884,"score_gpt":0.318719065549013,"score_spread":0.3009868805880742,"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."}}