{"id":"W4200008328","doi":"10.1158/1535-7163.targ-21-p096","title":"Abstract P096: Using CRISPR-Cas9 screens to identify microRNA involved in aggressive prostate cancer phenotypes","year":2021,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"CRISPR; microRNA; DU145; Prostate cancer; Cas9; Biology; Cancer research; In silico; Computational biology; Cancer; Gene knockout; Metastasis; Guide RNA; Phenotype; Gene; Bioinformatics; Genetics; LNCaP","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005265205,0.000847907,0.0006084391,0.0005561069,0.0004245807,0.0008203154,0.0006384748,0.0007551166,0.004277573],"category_scores_gemma":[0.0004159836,0.0003839511,0.0006639906,0.0003488383,0.0003806347,0.0002307007,0.0006171662,0.001194869,0.00182592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005230817,"about_ca_system_score_gemma":0.0005019703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001168809,"about_ca_topic_score_gemma":0.002256998,"domain_scores_codex":[0.9994224,0.00006512165,0.00006324761,0.0001339087,0.0002503205,0.000065035],"domain_scores_gemma":[0.9995449,0.0001272839,0.0001012989,0.00005035958,0.00007295035,0.0001032642],"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":[0.0001356633,0.00004612407,0.00041222,0.0001236661,0.00002810943,0.0001958299,0.00002199423,0.0004787017,0.9948114,0.0002579918,0.0006959006,0.002792282],"study_design_scores_gemma":[0.00004691909,0.0004112898,0.00355713,0.00001825877,0.00006512113,0.0008378078,0.00003227196,0.0051185,0.9780999,0.000145117,0.01163486,0.00003280528],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7851229,0.001818049,0.1621697,0.0009519389,0.0004172847,0.001038855,0.0211204,0.0097587,0.01760222],"genre_scores_gemma":[0.8803301,0.0009307679,0.0848649,0.0005107313,0.00003323916,0.0005435495,0.01402586,0.001520185,0.01724065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004277573,"threshold_uncertainty_score":0.01430994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02578215381947352,"score_gpt":0.3637751518388874,"score_spread":0.3379929980194138,"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."}}