{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003260694,0.0003068659,0.0003640765,0.0007520671,0.0002548902,0.0005065423,0.000297269,0.0002453849,0.0007843931],"category_scores_gemma":[0.0009400675,0.0001901142,0.0005164947,0.0005266748,0.000414682,0.0003691349,0.0003505098,0.0003373341,0.00009004481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006099656,"about_ca_system_score_gemma":0.0003576555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002932966,"about_ca_topic_score_gemma":0.004554096,"domain_scores_codex":[0.9997101,0.00008868315,0.00001054377,0.0001096723,0.00005236605,0.00002865353],"domain_scores_gemma":[0.9995521,0.0002514786,0.000092123,0.00003174508,0.0000393084,0.00003320618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004653535,0.0002418761,0.07545983,0.0002522905,0.0006019954,0.001166642,0.00027554,0.6370305,0.16623,0.04386122,0.001211839,0.07320298],"study_design_scores_gemma":[0.000008218602,0.00005941472,0.02650772,0.0000044231,0.00007261318,0.0001283504,0.000044048,0.9527299,0.006481643,0.01310493,0.0008408937,0.00001766869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8364633,0.0002212967,0.1599424,0.0001874109,0.000007742754,0.00003062929,0.0004085672,0.0003682295,0.00237038],"genre_scores_gemma":[0.9841284,0.0000797912,0.01503784,0.00002585803,0.000004541172,0.00002109143,0.0002104226,0.00001727933,0.0004746511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002932966,"threshold_uncertainty_score":0.005831778,"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."}}