{"id":"W2528057066","doi":"10.1186/s12859-016-1281-5","title":"VennDiagramWeb: a web application for the generation of highly customizable Venn and Euler diagrams","year":2016,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Natural Sciences and Engineering Research Council of Canada; Department of Medical Biophysics, University of Toronto; Canadian Institutes of Health Research; Prostate Cancer Canada; Government of Ontario; Movember Foundation; Terry Fox Research Institute; University of Toronto","keywords":"Venn diagram; Computer science; Visualization; Web application; Upload; Wizard; Documentation; Graphics; Pipeline (software); Data visualization; Interface (matter); Programming language; Data mining; World Wide Web; Computer graphics (images); Operating system","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":[0.0002415139,0.00008474634,0.00009766818,0.00003109445,0.00005686712,0.00001794662,0.0001212597,0.0000843822,0.000001912261],"category_scores_gemma":[0.000112528,0.00004924789,0.00006735735,0.0000599058,0.00008000813,0.00001059468,0.00005430856,0.00001914934,0.000003998453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000862652,"about_ca_system_score_gemma":0.00003993731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005736762,"about_ca_topic_score_gemma":0.00005424959,"domain_scores_codex":[0.9994678,0.00001256331,0.0002430184,0.00009895176,0.00007251143,0.0001051015],"domain_scores_gemma":[0.9993173,0.00005490745,0.0001581279,0.0003454412,0.00009788926,0.00002637496],"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":[0.00004422918,0.00004372832,0.001880062,0.0001049997,0.00007007276,3.359506e-8,0.00005843278,0.00005147302,0.8979584,0.0007095867,0.01437138,0.08470761],"study_design_scores_gemma":[0.000744902,0.0001737772,0.0003907516,0.00001545541,0.0001060098,0.00000368581,0.00005161894,0.1170588,0.7783436,0.0001511673,0.1027788,0.0001813359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0540345,0.0003784886,0.9446825,0.0001188947,0.00002106132,0.0004889978,0.00002029762,0.00001821449,0.0002370067],"genre_scores_gemma":[0.9517791,0.0008233078,0.04646418,0.0001228913,0.0001714216,0.0002111254,0.0001299883,0.00001626987,0.0002817111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8982183,"threshold_uncertainty_score":0.2008271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387613324609165,"score_gpt":0.2475971705070361,"score_spread":0.2337210372609444,"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."}}