{"id":"W4414619566","doi":"10.1093/genetics/iyaf209","title":"Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning","year":2025,"lang":"en","type":"article","venue":"Genetics","topic":"Fungal and yeast genetics research","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; Canada Research Chairs; Canada Foundation for Innovation; Nvidia","keywords":"Genome; Gene; Genomics; Comparative genomics; Functional genomics; Regulatory sequence; Gene regulatory network; Regulation of gene expression; Conserved sequence; DNA binding site","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007586538,0.000903746,0.0004775529,0.001208921,0.0002775535,0.0008850131,0.0008776832,0.0008421564,0.0005980888],"category_scores_gemma":[0.003125689,0.0003432391,0.0009338729,0.0006742952,0.0006680033,0.001024186,0.0005312357,0.001215006,0.0002335967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007910308,"about_ca_system_score_gemma":0.0006465591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003931228,"about_ca_topic_score_gemma":0.004801529,"domain_scores_codex":[0.9994903,0.000181012,0.00002302048,0.000198957,0.00007056603,0.00003621122],"domain_scores_gemma":[0.9984717,0.001096412,0.0001770168,0.0001387872,0.00008499444,0.00003108723],"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":[0.0001521368,0.0001618393,0.003829212,0.0001287913,0.0001059276,0.0001548535,0.00009471051,0.8035924,0.01602577,0.00751054,0.0009486207,0.1672952],"study_design_scores_gemma":[0.00000514321,0.00001228424,0.0003249141,0.000003824691,0.0000075145,0.00001323297,0.000007080586,0.9909116,0.001314711,0.00722454,0.0001715243,0.000003661276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06875854,0.0003737304,0.927961,0.000243928,0.00001214729,0.00003494419,0.0005186702,0.001578576,0.0005184336],"genre_scores_gemma":[0.633044,0.0004388964,0.3628119,0.0001576302,0.00004698209,0.0001176597,0.002454578,0.0001108224,0.0008174727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003931228,"threshold_uncertainty_score":0.007816672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009168230307779659,"score_gpt":0.2654587459337123,"score_spread":0.2562905156259326,"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."}}