{"id":"W2404451320","doi":"10.1158/1538-7445.fbcr15-pr03","title":"Abstract PR03: High-resolution detection of fitness genes and genotype-specific cancer vulnerabilities with CRISPR-Cas9 screens","year":2016,"lang":"en","type":"article","venue":"Cancer Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto","funders":"","keywords":"Biology; CRISPR; Gene; Genetics; Gene knockout; Context (archaeology); Computational biology; RNA interference; RNA","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":[0.0004938895,0.0007913652,0.0006721174,0.0008721922,0.0003283798,0.0006305547,0.0007799121,0.0006518566,0.004641321],"category_scores_gemma":[0.0004394069,0.0003826332,0.0007209778,0.0005658344,0.000381206,0.0002283351,0.0007661946,0.0009193027,0.001974249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004894403,"about_ca_system_score_gemma":0.0004436841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001486529,"about_ca_topic_score_gemma":0.00291572,"domain_scores_codex":[0.9992902,0.00007347637,0.0000899244,0.0001542254,0.0003011439,0.00009099246],"domain_scores_gemma":[0.9996069,0.0001234032,0.00008349375,0.0000819661,0.00005023195,0.0000539234],"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":[0.00005541238,0.00002531487,0.0003059554,0.00005067082,0.00001144663,0.0001086111,0.00001094557,0.000202131,0.9955499,0.0001874133,0.0003015284,0.003190735],"study_design_scores_gemma":[0.00002286916,0.0001693547,0.003445333,0.000009189464,0.00002635676,0.001096262,0.00001731932,0.002576844,0.9873466,0.0001098674,0.005159913,0.00002017761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7262602,0.001377328,0.2277326,0.0005022127,0.00009755884,0.0009445773,0.02055248,0.00952116,0.01301185],"genre_scores_gemma":[0.8344199,0.0008854349,0.131265,0.0003219923,0.00001310094,0.0006360454,0.01524436,0.001826559,0.01538754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004641321,"threshold_uncertainty_score":0.01552677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03324428774433177,"score_gpt":0.3577820944463337,"score_spread":0.3245378067020019,"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."}}