{"id":"W1990282010","doi":"10.3390/genes6010024","title":"Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease","year":2015,"lang":"en","type":"article","venue":"Genes","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Aging; National Institutes of Health; American Cancer Society; Howard Hughes Medical Institute","keywords":"Phenomics; Phenotype; Biology; Genetics; Gene; Computational biology; Gene regulatory network; Allele; Disease; Genome-wide association study; Genotype-phenotype distinction; Genotype; Genomics; Genome; Gene expression; Single-nucleotide polymorphism; Medicine","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":[0.0001363893,0.0001451379,0.0001052327,0.00001964889,0.00007344564,0.00005659315,0.0001458966,0.00009908821,0.000002799686],"category_scores_gemma":[0.00000390636,0.0001381,0.00007655406,0.00001926075,0.00002327748,0.00001279565,0.00008658744,0.00004584005,0.000002217687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002350396,"about_ca_system_score_gemma":0.00004215533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008113142,"about_ca_topic_score_gemma":0.000003200694,"domain_scores_codex":[0.9992958,0.00001869656,0.000154701,0.0002451391,0.00006589673,0.0002198117],"domain_scores_gemma":[0.9993992,0.000001656003,0.00005624626,0.0002771049,0.00005000203,0.0002157569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008198186,0.000200329,0.0002401555,0.00001981927,0.00007760072,6.46661e-7,0.000318219,0.9369547,0.04890819,0.000128889,0.001951226,0.01038046],"study_design_scores_gemma":[0.0005727607,0.0001258858,0.000008383533,0.000002930939,0.00001992066,0.000005788534,0.0009200331,0.977707,0.01697042,0.0000834737,0.00336835,0.0002150647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4771323,0.002745694,0.5192777,0.00001118735,0.0003358435,0.0002306246,0.00002137342,0.00001280542,0.0002324801],"genre_scores_gemma":[0.985021,0.00007237303,0.01224452,0.0001960606,0.001158892,0.0000798341,0.001089646,0.00002940501,0.0001082864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5078887,"threshold_uncertainty_score":0.5631555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04673541343352963,"score_gpt":0.2728612951225108,"score_spread":0.2261258816889812,"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."}}