{"id":"W2139830657","doi":"10.1093/nar/gkp820","title":"DRYGIN: a database of quantitative genetic interaction networks in yeast","year":2009,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Ontario Genomics; Ontario Genomics Institute; Genome Canada","keywords":"Biology; Computational biology; Genome; Annotation; Gene interaction; Ontology; Gene ontology; Interface (matter); Genetic network; Genetic screen; Gene; Interaction network; Genetics; Phenotype; Gene expression","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":[0.0006251464,0.00009811632,0.0001353707,0.0001399981,0.00005182055,0.00002348518,0.0002536518,0.0001354994,0.00003978385],"category_scores_gemma":[0.00007754697,0.00009467379,0.00004763185,0.0002858265,0.0001195364,0.00000866942,0.0001316453,0.000324788,0.00001915138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002669577,"about_ca_system_score_gemma":0.00006171191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004350239,"about_ca_topic_score_gemma":0.00006648839,"domain_scores_codex":[0.9988258,0.000114804,0.0002880713,0.0002194593,0.0002072502,0.0003446559],"domain_scores_gemma":[0.9993363,0.00002375678,0.00006134732,0.0003680716,0.0001364011,0.00007410871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002391262,0.0009318958,0.009759123,0.0001627119,0.0001447407,0.00005099463,0.001594539,0.01189132,0.7037382,0.007444827,0.02883467,0.2330558],"study_design_scores_gemma":[0.00766557,0.01105178,0.2279453,0.001024716,0.00004877819,0.0001373025,0.007301154,0.5885167,0.06737647,0.00642543,0.08059869,0.001908121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813612,0.001018347,0.0110283,0.0002021893,0.0001043924,0.0003102128,0.00001840138,0.000006206169,0.005950819],"genre_scores_gemma":[0.9957522,0.0004891337,0.003301213,0.00008873681,0.0001161551,0.000008154115,0.00007690844,0.00001137133,0.0001561696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6363617,"threshold_uncertainty_score":0.3860685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.038317279845149,"score_gpt":0.3525779688748422,"score_spread":0.3142606890296932,"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."}}