{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030002,0.0001981771,0.0001570905,0.0001373149,0.0004934763,0.0001067112,0.0004177407,0.0003458894,0.0002298325],"category_scores_gemma":[0.0001187017,0.0002082475,0.00009804316,0.0002202523,0.0002940452,0.000005115102,0.0004351526,0.0007361103,0.00005164559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003494548,"about_ca_system_score_gemma":0.0002371053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001159876,"about_ca_topic_score_gemma":0.00007749882,"domain_scores_codex":[0.9975568,0.0001516538,0.0002709271,0.000625319,0.0005561402,0.0008391301],"domain_scores_gemma":[0.9985442,0.0000271974,0.00005591874,0.0007119949,0.0004039372,0.0002568135],"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":[0.00003194557,0.00005211518,0.01110084,0.00001359419,0.00004994633,0.000007712649,0.0001004297,0.03485522,0.9526224,0.00002314862,0.0002847463,0.000857964],"study_design_scores_gemma":[0.002808718,0.001118134,0.2582397,0.00002284834,0.0001236242,0.0006636256,0.0004681303,0.2580911,0.414421,0.001495294,0.06051716,0.002030621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9344933,0.001162107,0.06307463,0.0000700064,0.0004359608,0.0002778115,0.00001372158,0.0000210426,0.0004514313],"genre_scores_gemma":[0.9882545,0.0002277563,0.007500137,0.00009207381,0.00273602,0.00002406457,0.0001302484,0.0000655871,0.0009696128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5382013,"threshold_uncertainty_score":0.8492087,"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."}}