{"id":"W2981570282","doi":"10.1016/j.cels.2019.09.009","title":"Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network","year":2019,"lang":"en","type":"article","venue":"Cell Systems","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"National Cancer Institute; National Institutes of Health; National Health Research Institutes; Canada Excellence Research Chairs, Government of Canada; National Human Genome Research Institute; Canadian Cancer Society; Ontario Research Foundation; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics Institute; Québec Consortium for Drug Discovery; Cancer Research Society","keywords":"Biology; Computational biology; Gene; Phenotype; Genetics; Transporter; Gene regulatory network; Systems biology; Biological network; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002471528,0.0001853707,0.0003010322,0.00005268391,0.00005817218,0.00006399356,0.0002182473,0.0001827379,0.00001858016],"category_scores_gemma":[0.000004052371,0.0001731502,0.0001888244,0.0002124074,0.00002322003,0.000007499861,0.00004114263,0.0001216972,0.0001032823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002280725,"about_ca_system_score_gemma":0.0000345873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001043583,"about_ca_topic_score_gemma":0.0000238666,"domain_scores_codex":[0.998718,0.00006880235,0.0004753497,0.0003083116,0.0001274658,0.0003020866],"domain_scores_gemma":[0.9990568,0.00001305637,0.0002371476,0.0004894804,0.00008104341,0.0001224686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002971954,0.0002465433,0.2100118,0.0005892933,0.00163845,0.00001163895,0.0008038812,0.6403294,0.1261488,0.0002927782,0.01871907,0.0009112781],"study_design_scores_gemma":[0.01225411,0.001316617,0.13926,0.0005100411,0.002821244,0.00007370766,0.001439415,0.2632838,0.0130119,0.0002780582,0.561542,0.004209198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893017,0.001303297,0.00578169,0.00001190748,0.002103741,0.000452708,0.00001333441,0.00001649198,0.001015142],"genre_scores_gemma":[0.9966474,0.0001021694,0.0003307594,0.0001240718,0.0005300827,0.00002607459,0.000151308,0.00002030013,0.002067794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5428229,"threshold_uncertainty_score":0.7060862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004415705802271359,"score_gpt":0.204497858946525,"score_spread":0.2000821531442536,"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."}}