{"id":"W2345152897","doi":"10.1371/journal.pone.0154315","title":"VennPainter: A Tool for the Comparison and Identification of Candidate Genes Based on Venn Diagrams","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum","funders":"Kunming Institute of Zoology, Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Venn diagram; Identification (biology); Computational biology; Genetics; Biology; Candidate gene; Gene; Bioinformatics; Computer science; Mathematics","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":[0.007015963,0.002723603,0.003593275,0.009462985,0.002021365,0.002999716,0.004695296,0.001505416,0.06239087],"category_scores_gemma":[0.01455981,0.002349272,0.002312235,0.006757082,0.0009761638,0.004130569,0.004338446,0.00357852,0.01329958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009471402,"about_ca_system_score_gemma":0.002869206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002058814,"about_ca_topic_score_gemma":0.002713949,"domain_scores_codex":[0.9966615,0.001170036,0.0003971561,0.0008110697,0.0007446954,0.0002154238],"domain_scores_gemma":[0.9919321,0.005816757,0.0006616649,0.0006114313,0.0006498809,0.0003281588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002619476,0.000482083,0.009851767,0.01468258,0.001779994,0.001533972,0.003060139,0.01332632,0.03810976,0.03387335,0.530591,0.3500896],"study_design_scores_gemma":[0.001392582,0.0004943817,0.01684783,0.002353896,0.0008385539,0.002721564,0.0008096313,0.1384058,0.04391098,0.08640241,0.7047424,0.001079974],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01032888,0.002389128,0.7050471,0.0003463727,0.0006544292,0.001180582,0.06571206,0.2078661,0.006475316],"genre_scores_gemma":[0.03076464,0.001069053,0.8749422,0.0003834493,0.000133355,0.003701995,0.05749904,0.02799925,0.003506982],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06239087,"threshold_uncertainty_score":0.2087182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661432373969022,"score_gpt":0.2224876390242963,"score_spread":0.2058733152846061,"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."}}