{"id":"W2910111050","doi":"10.1038/s41596-018-0103-9","title":"Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap","year":2019,"lang":"en","type":"article","venue":"Nature Protocols","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2143,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; Ontario Genomics; Hospital for Sick Children; University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Cancer Institute; National Human Genome Research Institute","keywords":"Computational biology; Troubleshooting; Visualization; Protocol (science); Biology; Genomics; Genome; Functional genomics; Genome browser; Bioinformatics; Gene; Computer science; Data mining; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001387406,0.001926055,0.001398869,0.005655225,0.00143718,0.001823803,0.001366089,0.0005379622,0.02428288],"category_scores_gemma":[0.002328502,0.0004988821,0.001942118,0.005023647,0.0003064677,0.0009389914,0.001524087,0.002353807,0.00436612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005203709,"about_ca_system_score_gemma":0.002034843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005804073,"about_ca_topic_score_gemma":0.009606498,"domain_scores_codex":[0.9992388,0.0001179801,0.00006676021,0.0002188253,0.0002301762,0.0001274843],"domain_scores_gemma":[0.9992679,0.0002907401,0.00006229033,0.0001082441,0.0001876943,0.00008315391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002357958,0.0008025594,0.01858263,0.01174091,0.002809254,0.0011844,0.002826707,0.01505739,0.3534931,0.01931053,0.2227131,0.3491215],"study_design_scores_gemma":[0.0005014889,0.0004425908,0.07432981,0.0008150738,0.001341717,0.001223355,0.001152499,0.143122,0.3705846,0.04572939,0.3600671,0.0006903961],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1015682,0.001675259,0.3271194,0.00153686,0.0007032536,0.001300862,0.4229538,0.1303796,0.01276293],"genre_scores_gemma":[0.1323654,0.00157175,0.6857592,0.0005899009,0.0001051666,0.003488251,0.1500243,0.0140417,0.01205424],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02428288,"threshold_uncertainty_score":0.08123428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421578793025818,"score_gpt":0.3144017545757625,"score_spread":0.3001859666455043,"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."}}