{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001404952,0.00004699938,0.00006964627,0.00001328252,0.00002549021,0.000005877935,0.00004878038,0.00003104248,0.000001620392],"category_scores_gemma":[0.00009424064,0.00002800697,0.00002225047,0.00002002882,0.00002853279,0.0000011193,0.000009538205,0.00001365542,9.299343e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003255954,"about_ca_system_score_gemma":0.000006220189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004071517,"about_ca_topic_score_gemma":0.000004065449,"domain_scores_codex":[0.9996604,0.00001247841,0.0001044037,0.0001179245,0.00004325534,0.00006150849],"domain_scores_gemma":[0.9997147,0.00001179268,0.00004918768,0.0001724333,0.00004096059,0.00001092061],"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":[0.00006744426,0.0001119625,0.001002754,0.00003719676,0.00004030756,1.282994e-8,0.000008185727,0.00002122676,0.9891033,0.00001595269,0.0001014105,0.009490269],"study_design_scores_gemma":[0.0002409534,0.0001252719,0.002997833,0.0000369262,0.00004132024,1.784039e-7,0.000003079508,0.0007437416,0.9936497,0.000005704566,0.002109591,0.00004576527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869795,0.0005244334,0.01179289,0.0004196291,0.00005579078,0.00018482,0.00003495689,0.00000544202,0.000002507131],"genre_scores_gemma":[0.999105,0.0002035257,0.0002984323,0.00002370638,0.0001598359,0.0000324982,0.00002010414,0.000006493565,0.0001503484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01212553,"threshold_uncertainty_score":0.1142091,"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."}}