{"id":"W2611042446","doi":"10.1101/134346","title":"Coessentiality and cofunctionality: a network approach to learning genetic vulnerabilities from cancer cell line fitness screens","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"University of Texas MD Anderson Cancer Center; Cancer Prevention and Research Institute of Texas","keywords":"Synthetic lethality; Biology; Computational biology; Gene; Genetics; Small hairpin RNA; Biological network; Context (archaeology); CRISPR; DNA repair; Gene knockdown","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":[0.001309818,0.0006174725,0.0005010148,0.003000356,0.0003757738,0.001048678,0.0004761912,0.0004134568,0.001160125],"category_scores_gemma":[0.004510338,0.0002150204,0.0006712983,0.001372128,0.000655004,0.0006662821,0.0008323626,0.0009362119,0.00013859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000998468,"about_ca_system_score_gemma":0.0005169249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002219109,"about_ca_topic_score_gemma":0.003374676,"domain_scores_codex":[0.9994789,0.0002222066,0.00002446162,0.0001579645,0.00008335974,0.0000331003],"domain_scores_gemma":[0.9968261,0.002133835,0.0004563421,0.0003154018,0.0001451747,0.0001231801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007921667,0.0005952625,0.1836053,0.0006439487,0.001235016,0.0008760747,0.0005288004,0.5190569,0.114273,0.04281024,0.003052709,0.1325305],"study_design_scores_gemma":[0.00001794231,0.00009129079,0.02197309,0.00002250938,0.00009821459,0.0001050901,0.0001090463,0.927035,0.005843873,0.04331405,0.001367859,0.0000220499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5132952,0.0004720588,0.4774563,0.0007509936,0.00002047712,0.0002078714,0.003925684,0.001361785,0.002509643],"genre_scores_gemma":[0.8506193,0.0002909257,0.1451244,0.0001153109,0.00002475863,0.0002097391,0.002827741,0.0001046824,0.0006832841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003000356,"threshold_uncertainty_score":0.007244408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490038973841416,"score_gpt":0.2262870663241507,"score_spread":0.2113866765857365,"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."}}