{"id":"W3103010508","doi":"10.1101/457101","title":"Personalized and graph genomes reveal missing signal in epigenomic data","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics; McGill University and Génome Québec Innovation Centre; McGill University; McGill Genome Centre","funders":"Canadian Institutes of Health Research; Compute Canada","keywords":"Epigenomics; Genome; Graph; Computer science; Reference genome; Computational biology; Biology; Genetics; Gene; Theoretical computer science; DNA methylation","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":[0.001457468,0.0003338719,0.0004497581,0.00118125,0.0003492629,0.0007237918,0.0004840537,0.0008022548,0.002194706],"category_scores_gemma":[0.006124244,0.0003387796,0.0005955527,0.001352092,0.0004220818,0.0006288601,0.0006886307,0.0007423367,0.0004375256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004906349,"about_ca_system_score_gemma":0.000258795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349016,"about_ca_topic_score_gemma":0.003187625,"domain_scores_codex":[0.9985089,0.0003759086,0.00007785906,0.000534648,0.0003742003,0.0001284771],"domain_scores_gemma":[0.9960302,0.002210545,0.0004668013,0.0008501076,0.000319576,0.0001228402],"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":[0.002603986,0.0001861775,0.1660039,0.001124676,0.001353201,0.0007365225,0.0007037148,0.03785153,0.6899818,0.006378568,0.004149244,0.08892655],"study_design_scores_gemma":[0.0001084968,0.0005428846,0.4253893,0.00009401578,0.0008638575,0.002220276,0.0006330854,0.2059635,0.3201328,0.0183522,0.0255077,0.0001918719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9103609,0.0007430357,0.07792085,0.0002316716,0.00008113636,0.00005117596,0.007025133,0.002093351,0.001492794],"genre_scores_gemma":[0.9245384,0.0001423643,0.06406188,0.0002145397,0.00002325698,0.00005749651,0.009934393,0.0003764502,0.0006511419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002194706,"threshold_uncertainty_score":0.007707894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02318386789056699,"score_gpt":0.2387067122535048,"score_spread":0.2155228443629378,"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."}}