{"id":"W2036956532","doi":"10.1101/gr.102749.109","title":"Predicting genetic modifier loci using functional gene networks","year":2010,"lang":"en","type":"article","venue":"Genome Research","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Research Resources; National Institute of General Medical Sciences; Ministerio de Ciencia e Innovación; Institució Catalana de Recerca i Estudis Avançats; National Research Foundation of Korea; National Research Foundation; Agència de Gestió d'Ajuts Universitaris i de Recerca; National Institutes of Health; Yonsei University; National Science Foundation","keywords":"Biology; Genetics; Gene; Computational biology; Epistasis; Caenorhabditis elegans; Phenotype; Locus (genetics); Gene regulatory network; Model organism; Gene expression","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":[0.0005434355,0.001119011,0.0004885917,0.002568155,0.0002855016,0.0005711888,0.0005425626,0.000528399,0.00121137],"category_scores_gemma":[0.002299593,0.0002916775,0.0006495756,0.001302533,0.0003208331,0.000609591,0.000440368,0.000336262,0.0002622408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006843093,"about_ca_system_score_gemma":0.0004890402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004212902,"about_ca_topic_score_gemma":0.006058469,"domain_scores_codex":[0.9997136,0.00006535306,0.00001650689,0.0001111132,0.0000725759,0.00002079442],"domain_scores_gemma":[0.9988525,0.0007315161,0.00020126,0.00006242496,0.0001034902,0.00004880521],"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":[0.0004296908,0.0001791934,0.08871456,0.0004371932,0.000493703,0.0007403081,0.00009826657,0.7233636,0.09237885,0.007179403,0.001475866,0.08450947],"study_design_scores_gemma":[0.000022051,0.00005749646,0.02075054,0.0000199943,0.0002005528,0.0001882646,0.00003628463,0.9495128,0.01449824,0.01185291,0.002835253,0.00002552666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3866767,0.001080855,0.5998597,0.0002843344,0.00002127813,0.00009853135,0.007972172,0.001816116,0.002190334],"genre_scores_gemma":[0.8199742,0.0008044552,0.1685384,0.00006687118,0.00001735432,0.0001341531,0.009226244,0.0001211623,0.001117077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004212902,"threshold_uncertainty_score":0.008376777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05443584129745688,"score_gpt":0.3119221869642298,"score_spread":0.2574863456667729,"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."}}