{"id":"W4416644676","doi":"10.7554/elife.92497.3","title":"Exploiting fluctuations in gene expression to detect causal interactions between genes","year":2025,"lang":"","type":"article","venue":"eLife","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Connaught Fund; University of Toronto","keywords":"Relation (database); Set (abstract data type); Gene; Population; Gene regulatory network; Gene expression; Regulation of gene expression; Process (computing)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003849199,0.0003623207,0.0004371165,0.0004936029,0.0003184449,0.0000803215,0.0003758849,0.0002443436,0.00006642338],"category_scores_gemma":[0.0002739165,0.0004198754,0.000258142,0.0009787283,0.0000578572,0.0000123303,0.0005364429,0.0002482312,0.00005915167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465041,"about_ca_system_score_gemma":0.0003125476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005458956,"about_ca_topic_score_gemma":0.0004507069,"domain_scores_codex":[0.997243,0.0002754267,0.0007935666,0.0008701395,0.0002606991,0.0005571432],"domain_scores_gemma":[0.9985012,0.00007528924,0.0001651615,0.0008150028,0.0002169816,0.0002263906],"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":[0.00003460354,0.00004348486,0.02326649,0.00002367561,0.0002314765,0.000004135042,0.0001167556,0.01096027,0.9369043,0.000005881524,0.001866362,0.02654259],"study_design_scores_gemma":[0.0003874379,0.00006473672,0.02102624,0.0001519476,0.0001840885,0.000002041621,0.000241996,0.0005149079,0.9488247,0.00004008143,0.02817975,0.0003820592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482681,0.003816109,0.04576379,0.0006003582,0.0005183678,0.0003762743,0.00003371449,0.00002092529,0.000602387],"genre_scores_gemma":[0.9903151,0.0005747685,0.005850082,0.0003327269,0.001066524,0.0001191122,0.0001758272,0.00004000671,0.001525888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04204699,"threshold_uncertainty_score":0.9998253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515256394156485,"score_gpt":0.2934880861137157,"score_spread":0.2783355221721509,"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."}}