{"id":"W2800195468","doi":"10.1016/j.csbj.2018.04.002","title":"Corrigendum to “A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer”","year":2018,"lang":"en","type":"erratum","venue":"Computational and Structural Biotechnology Journal","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Food and Drug Administration; Arkansas Science and Technology Authority; National Institutes of Health; U.S. Food and Drug Administration; Hamilton Health Sciences Foundation; Arkansas Research Alliance","keywords":"Gene; Computational biology; Expression quantitative trait loci; Kidney cancer; Pathway analysis; Disease; Biology; Cancer; Bioinformatics; Medicine; Genetics; Gene expression; Internal medicine; Single-nucleotide polymorphism","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001191062,0.0004397235,0.0008257689,0.001524578,0.000289693,0.00008617575,0.0001634678,0.0005666003,0.00006383384],"category_scores_gemma":[0.00009376691,0.0003488683,0.0001934429,0.0006765799,0.0003165711,0.00005395191,0.000131748,0.0009949481,0.000002743723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003906422,"about_ca_system_score_gemma":0.001007581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006902013,"about_ca_topic_score_gemma":0.00004775793,"domain_scores_codex":[0.9979135,0.00006364463,0.0005428144,0.000647579,0.0004171388,0.0004153237],"domain_scores_gemma":[0.9984667,0.00002895449,0.0002827919,0.0002158214,0.0002694747,0.0007362003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005135778,0.0003756901,0.5214984,0.00251576,0.01136987,0.01703398,0.0008753721,0.0129237,0.0130782,0.00126555,0.1845945,0.2293332],"study_design_scores_gemma":[0.003384589,0.0009587095,0.8842272,0.0009548856,0.002939759,0.001911836,0.00005791612,0.08909844,0.001074208,0.005805738,0.008676502,0.0009102134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620599,0.01876945,0.00178351,0.01138389,0.004805642,0.0005356588,0.0005498994,0.00007301005,0.00003898856],"genre_scores_gemma":[0.9774204,0.001854031,0.01454681,0.001629366,0.001504197,0.00003517608,0.001292156,0.00005643977,0.001661377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3627288,"threshold_uncertainty_score":0.9998963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02471000919419946,"score_gpt":0.2694117688323721,"score_spread":0.2447017596381726,"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."}}