{"id":"W3214304248","doi":"10.1073/pnas.2115116118","title":"CRISPR-SID: Identifying EZH2 as a druggable target for desmoid tumors via in vivo dependency mapping","year":2021,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Chromatin Remodeling and Cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"National Cancer Institute; Bijzonder Onderzoeksfonds UGent; Stand Up To Cancer; Vlaamse regering; Universiteit Gent; Fonds Wetenschappelijk Onderzoek; Agentschap Innoveren en Ondernemen; Kom op tegen Kanker; Agentschap voor Innovatie door Wetenschap en Technologie; Desmoid Tumor Research Foundation","keywords":"CRISPR; Druggability; Wnt signaling pathway; Computational biology; Biology; Cancer research; Carcinogenesis; Genome editing; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002008794,0.000324868,0.0002666161,0.0002195511,0.0001774583,0.0002815923,0.0002862315,0.0003103997,0.002041264],"category_scores_gemma":[0.0001483987,0.0001844851,0.0002233297,0.0001225443,0.0002720716,0.0001446109,0.0003194599,0.0005327734,0.0003365894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003641149,"about_ca_system_score_gemma":0.0002551786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008209618,"about_ca_topic_score_gemma":0.001983644,"domain_scores_codex":[0.9998952,0.000008808819,0.000006730706,0.00003033375,0.00004234367,0.00001658733],"domain_scores_gemma":[0.9999321,0.0000162443,0.00001875669,0.00000900976,0.000005964694,0.00001797929],"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":[0.00003140559,0.000008886215,0.0002714905,0.00002429842,0.000006136195,0.00004857614,0.000005234314,0.0004276964,0.9958686,0.0003236284,0.0001237459,0.002860328],"study_design_scores_gemma":[0.00001004291,0.00006110473,0.001065358,0.000001674767,0.000008631916,0.0001944968,0.000006444459,0.003917004,0.9921486,0.0001043653,0.002477192,0.00000493745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8778601,0.001353506,0.1078292,0.0005983209,0.0001134286,0.0001681027,0.001949834,0.002225421,0.007902178],"genre_scores_gemma":[0.9632539,0.0006053965,0.03000793,0.0001195617,0.000009352995,0.00005331954,0.0007270365,0.00009874281,0.005124755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002041264,"threshold_uncertainty_score":0.006828666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03508239185839815,"score_gpt":0.3223301042785175,"score_spread":0.2872477124201194,"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."}}