{"id":"W4317743512","doi":"10.1093/nargab/lqad003","title":"Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression","year":2023,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; SickKids Foundation; University of Toronto","funders":"National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Centre for Applied Genomics; Genome Canada","keywords":"Computational biology; Expression (computer science); Pipeline (software); Gene expression; Gene; Computer science; Differential (mechanical device); Biology; Gene expression profiling; DEET; Information retrieval; Data mining; Genetics","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":[0.0001578888,0.0002920839,0.0003149807,0.0001312535,0.0002266876,0.00008692497,0.0002873633,0.0002808159,0.00003959175],"category_scores_gemma":[0.00001222912,0.0002434607,0.0001258438,0.00009808125,0.0001031013,0.00003099788,0.0005537,0.0001643352,0.00001238434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002488581,"about_ca_system_score_gemma":0.00004460683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006351647,"about_ca_topic_score_gemma":0.000004141311,"domain_scores_codex":[0.9984198,0.00003473378,0.0007158102,0.0002493916,0.0002126412,0.000367679],"domain_scores_gemma":[0.9989069,0.00001635577,0.0003744986,0.0004679522,0.00008381408,0.0001505508],"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":[0.0001380665,0.0000880434,0.0002768489,0.0000987852,0.00005250027,0.000001051162,0.001123964,0.0000884308,0.9885929,0.00006934321,0.001500469,0.007969625],"study_design_scores_gemma":[0.001982508,0.001051702,0.001921365,0.0000927022,0.00005957377,0.00001806494,0.001504599,0.0349923,0.9538152,0.0003769824,0.003533858,0.000651206],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895909,0.00005851988,0.009232264,0.00001575568,0.0003022024,0.0003372667,0.0001255134,0.00002929692,0.0003082129],"genre_scores_gemma":[0.9924927,0.0004864483,0.005056306,0.00005549724,0.0003112601,0.00001797613,0.001355726,0.00003000235,0.0001940442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03490387,"threshold_uncertainty_score":0.9928038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009721680046971246,"score_gpt":0.2465778461921381,"score_spread":0.2368561661451669,"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."}}