{"id":"W4412626392","doi":"10.1093/bioadv/vbaf178","title":"Gene-set enrichment analysis and visualization on the web using EnrichmentMap:RNASeq","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"National Institutes of Health","keywords":"Visualization; Computational biology; Set (abstract data type); Biology; Computer science; Genetics; World Wide Web; Data mining; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003097754,0.0001872814,0.0001498862,0.000144487,0.0001689028,0.0001927782,0.0001498666,0.00008965448,0.00002018437],"category_scores_gemma":[0.0000196759,0.0001249595,0.000105194,0.0004109621,0.00007398333,0.00002453351,0.0001063309,0.00007973934,0.00001553949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002458638,"about_ca_system_score_gemma":0.00005140438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003166964,"about_ca_topic_score_gemma":0.00001034579,"domain_scores_codex":[0.9990264,0.00002355634,0.0003677081,0.0001703138,0.0001868424,0.000225156],"domain_scores_gemma":[0.9994562,0.00003834651,0.0001160643,0.0002840689,0.00003820202,0.00006707675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005778306,0.0005289556,0.01207238,0.003048248,0.01450906,0.00003926508,0.009983886,0.1171194,0.1563597,0.114167,0.0581212,0.513473],"study_design_scores_gemma":[0.0002440625,0.0002286018,0.0001551495,0.00005518784,0.0003572051,0.00001744086,0.000608852,0.8155501,0.01317589,0.0004731761,0.1687197,0.0004146189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.701874,0.01874753,0.2710094,0.0004774583,0.001005716,0.0008801037,0.000277422,0.0001007596,0.005627567],"genre_scores_gemma":[0.9868916,0.005456336,0.006069229,0.0008155,0.0002207957,0.00002022333,0.0003003717,0.00001915516,0.0002068347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6984307,"threshold_uncertainty_score":0.5095699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282611854482546,"score_gpt":0.278922961500048,"score_spread":0.2660968429552225,"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."}}