{"id":"W2767673199","doi":"10.1158/1538-8514.synthleth-ia06","title":"Abstract IA06: Leveraging genome-wide CRISPR screens and synthetic lethal interactions for novel cancer therapeutics","year":2017,"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; Frizzled; Biology; Wnt signaling pathway; Cancer; Genome editing; Computational biology; Cas9; Genome; Context (archaeology); Cancer research; 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":[0.0008734975,0.0009958426,0.0008899639,0.001111415,0.0005262914,0.001742162,0.001187683,0.001325025,0.007623374],"category_scores_gemma":[0.0005135202,0.0004887934,0.0007984035,0.0005065068,0.000678314,0.0004451391,0.001365676,0.002351189,0.003386048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009317789,"about_ca_system_score_gemma":0.0008051205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009436777,"about_ca_topic_score_gemma":0.001636514,"domain_scores_codex":[0.9990393,0.0001363234,0.00009511067,0.0001898255,0.0004140626,0.0001253463],"domain_scores_gemma":[0.99959,0.00006648507,0.00008078852,0.00007905853,0.00008139257,0.0001023576],"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":[0.0001304573,0.00006210368,0.0001935558,0.0001268461,0.00002825794,0.000209404,0.00003040921,0.001047047,0.9807344,0.002523168,0.003171657,0.01174259],"study_design_scores_gemma":[0.00004211659,0.0003693522,0.0008450159,0.00002866345,0.00004838578,0.0007915518,0.00003014188,0.004950189,0.9445918,0.0005187056,0.04774166,0.00004234583],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3859129,0.007930432,0.4683912,0.003924659,0.002670457,0.001924681,0.01757377,0.03108731,0.08058461],"genre_scores_gemma":[0.6668583,0.005881227,0.2417725,0.001270503,0.0001557331,0.0009492819,0.008898241,0.002089841,0.0721243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007623374,"threshold_uncertainty_score":0.02550274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04052385716952246,"score_gpt":0.3562620291661898,"score_spread":0.3157381719966673,"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."}}