{"id":"W4308684495","doi":"10.1182/bloodadvances.2022008966","title":"Applying CRISPR-Cas9 screens to dissect hematological malignancies","year":2022,"lang":"en","type":"review","venue":"Blood Advances","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Toronto General Hospital; University of Toronto; University Health Network","funders":"","keywords":"CRISPR; Context (archaeology); Computational biology; Genome editing; Cas9; Biology; Disease; Epigenomics; Precision medicine; Computer science; Genetics; Gene; Medicine; Pathology; DNA methylation","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":[0.000137721,0.0004435724,0.0008728915,0.0000860327,0.0001452834,0.00003346104,0.0004642746,0.0001860222,0.00005428808],"category_scores_gemma":[0.000146844,0.0003772785,0.0003942615,0.0002253415,0.00003544539,0.000003009297,0.0004126027,0.0002161863,0.00002531005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001147012,"about_ca_system_score_gemma":0.00006607713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002497381,"about_ca_topic_score_gemma":0.00001025621,"domain_scores_codex":[0.9982054,0.00006505696,0.0004062339,0.0006954832,0.0002014165,0.0004263739],"domain_scores_gemma":[0.9991748,0.00005220062,0.0001163879,0.0004918957,0.00002061839,0.0001441124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001088681,0.0001131658,0.00003107916,0.006070985,0.0002389733,0.00008498233,0.00001957605,0.0001720962,0.0007330375,0.0001247401,0.001026437,0.991374],"study_design_scores_gemma":[0.00009407597,0.0001883094,0.000002300154,0.0006165936,0.0002664514,0.0002317506,0.00005712564,5.634905e-7,0.001505389,0.000009479106,0.9965323,0.0004957135],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006413345,0.9956027,0.002518898,0.000008855741,0.0002783349,0.0008753705,0.00008340007,0.0001001542,0.0004681619],"genre_scores_gemma":[0.0001263558,0.9948941,0.002442943,0.00007542201,0.0003316417,0.001523101,0.0001877077,0.0001151001,0.0003036136],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9955058,"threshold_uncertainty_score":0.9998679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0305616991961515,"score_gpt":0.3683949792914444,"score_spread":0.3378332800952929,"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."}}