{"id":"W2989760732","doi":"10.1111/pbi.13299","title":"Learning from methylomes: epigenomic correlates of <i>Populus balsamifera</i> traits based on deep learning models of natural DNA methylation","year":2019,"lang":"en","type":"article","venue":"Plant Biotechnology Journal","topic":"Bioenergy crop production and management","field":"Agricultural and Biological Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of British Columbia; University of Guelph; The Scarborough Hospital; University of Toronto","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; University of Guelph; Genome Canada","keywords":"Biology; DNA methylation; Epigenetics; Epigenomics; Population; Quantitative trait locus; Natural population growth; Genetic variation; Methylation; Genetics; Evolutionary biology; DNA; Gene; Computational biology; Gene expression","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":[],"consensus_categories":[],"category_scores_codex":[0.0004484951,0.0001618252,0.0003273281,0.000112755,0.0001484752,0.00002151552,0.0002715461,0.0002897744,0.0002138129],"category_scores_gemma":[0.00008197835,0.00007350692,0.0001263188,0.0002747047,0.00008057601,0.0001106345,0.00005406131,0.0007963094,0.00001375839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003622395,"about_ca_system_score_gemma":0.000008712377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008024222,"about_ca_topic_score_gemma":0.0000402971,"domain_scores_codex":[0.9986751,0.000178076,0.0004120816,0.000275992,0.0002215084,0.0002372178],"domain_scores_gemma":[0.9991471,0.0001858316,0.0004947366,0.00005695727,0.00007095924,0.00004447648],"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":[0.0001601434,0.00006768206,0.001798392,0.000007408859,0.00006807805,0.000003465398,0.00008105505,0.04232736,0.8220199,0.0006509849,0.00001535156,0.1328001],"study_design_scores_gemma":[0.001608898,0.002995931,0.04067326,0.0002329211,0.0001424221,0.00007246057,0.002881876,0.2168428,0.7209318,0.004787684,0.008125453,0.0007045039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967467,0.0008744961,0.0004690647,0.001181539,0.0003759052,0.0001170212,0.00001305792,0.00006968468,0.0001524995],"genre_scores_gemma":[0.9986222,0.0005342857,0.0005076225,0.00006260726,0.00006118736,0.000001673916,0.00007676605,0.000002186661,0.0001315105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1745155,"threshold_uncertainty_score":0.3459612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153958888068139,"score_gpt":0.1910208099685178,"score_spread":0.1756249211617039,"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."}}