{"id":"W4293548710","doi":"10.1101/2022.08.29.505468","title":"Differential Expression Enrichment Tool (DEET): An interactive atlas of human differential gene expression","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; SickKids Foundation; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Expression (computer science); Computer science; Computational biology; Differential (mechanical device); Metadata; Gene expression; Gene; Biology; RNA-Seq; Gene expression profiling; Information retrieval; Data mining; Genetics; Transcriptome; World Wide Web","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":[0.003039949,0.001324253,0.001802836,0.007610982,0.001106427,0.001717691,0.001910626,0.0005860687,0.02718973],"category_scores_gemma":[0.006236139,0.0008225964,0.001839997,0.006645439,0.0004577782,0.0008473325,0.002526383,0.001627394,0.005999525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008731189,"about_ca_system_score_gemma":0.001833522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001284832,"about_ca_topic_score_gemma":0.003117957,"domain_scores_codex":[0.9972787,0.0006972449,0.0002728485,0.0007483028,0.0008252817,0.0001775784],"domain_scores_gemma":[0.9969439,0.001689846,0.0003802557,0.0003995563,0.0004487156,0.0001375755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002302764,0.0002757841,0.03395753,0.01418841,0.002606164,0.001632156,0.001243442,0.01592518,0.1747542,0.02516582,0.4799332,0.2480154],"study_design_scores_gemma":[0.0009548154,0.0004633157,0.069246,0.001353638,0.001613138,0.002123529,0.0004377404,0.0625334,0.1088429,0.03527617,0.7167901,0.000365239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.04436887,0.007863766,0.387202,0.000915579,0.0005894653,0.0007233918,0.4644552,0.07734718,0.01653455],"genre_scores_gemma":[0.1094419,0.003124263,0.5869828,0.000901706,0.0001910932,0.004692092,0.2788732,0.009970872,0.005822107],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02718973,"threshold_uncertainty_score":0.09095871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009051569652437816,"score_gpt":0.2328509986494426,"score_spread":0.2237994289970048,"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."}}