{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008777041,0.0008896768,0.0005117979,0.0005821563,0.0005007818,0.0006715059,0.0009217804,0.0006824184,0.001289769],"category_scores_gemma":[0.002031274,0.0004202089,0.0008235392,0.0006210281,0.0009352535,0.000635796,0.0008603697,0.001044373,0.0001726247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006253523,"about_ca_system_score_gemma":0.0007221221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004135861,"about_ca_topic_score_gemma":0.003052127,"domain_scores_codex":[0.9997516,0.0001410757,0.00001041736,0.00004531737,0.00003656958,0.0000149893],"domain_scores_gemma":[0.9992772,0.0005256176,0.00007900515,0.00006263498,0.00002312187,0.00003244245],"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":[0.00008419179,0.00004244251,0.002101498,0.0001067541,0.00008940998,0.0001256011,0.00006623122,0.9090179,0.01875688,0.06311741,0.0004489623,0.006042598],"study_design_scores_gemma":[0.00001840899,0.00003195397,0.0004149278,0.000005234932,0.0000215435,0.00003038353,0.00002069056,0.9647915,0.0029075,0.02970526,0.002043244,0.000009457151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06026935,0.000424533,0.9338855,0.0004650741,0.00004444087,0.00008863716,0.0008553716,0.0004874967,0.003479572],"genre_scores_gemma":[0.5341235,0.002116942,0.4596972,0.0001603583,0.000055144,0.0006242764,0.0009142722,0.0001774939,0.002130714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004135861,"threshold_uncertainty_score":0.008223534,"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."}}