{"id":"W2850047394","doi":"10.1101/367748","title":"DNA Methylation Network Estimation with Sparse Latent Gaussian Graphical Model","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"CpG site; DNA methylation; Gene regulatory network; Graphical model; Computational biology; Latent variable; Gene; Computer science; Gaussian; Genomics; Curse of dimensionality; Biology; Data mining; Artificial intelligence; Genetics; Gene expression; Genome","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":[0.001844436,0.0007205036,0.0009381062,0.001144056,0.0003238873,0.0008467033,0.001575877,0.0011948,0.001475382],"category_scores_gemma":[0.007754283,0.0005504201,0.001059573,0.00127447,0.0008442565,0.001125014,0.0008603451,0.001804873,0.0005965206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055667,"about_ca_system_score_gemma":0.001026866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01142052,"about_ca_topic_score_gemma":0.01303205,"domain_scores_codex":[0.999032,0.0004947347,0.00002567689,0.0002371563,0.0001346955,0.00007565998],"domain_scores_gemma":[0.9966203,0.002474135,0.0002889917,0.0002801231,0.0002348406,0.0001016629],"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":[0.0001276942,0.00004382416,0.003304463,0.00006401236,0.00006653872,0.00006229661,0.00005758018,0.9471412,0.002281128,0.01504549,0.001806604,0.02999921],"study_design_scores_gemma":[0.000005988672,0.000003713702,0.0001616572,0.000002119257,0.000002480691,0.000005534313,0.000002590054,0.9928547,0.000184132,0.006659172,0.0001152144,0.000002909891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01877831,0.0001564996,0.9795131,0.0002897464,0.00001528723,0.00002286741,0.0003574588,0.0005912004,0.0002755159],"genre_scores_gemma":[0.6797866,0.0003583866,0.3134254,0.00032799,0.00009444815,0.0001924753,0.003259359,0.0001841809,0.002371185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01142052,"threshold_uncertainty_score":0.02270806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121660815281523,"score_gpt":0.2124132259394451,"score_spread":0.2011966177866299,"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."}}