{"id":"W3028658133","doi":"10.1186/s13059-020-02038-8","title":"Personalized and graph genomes reveal missing signal in epigenomic data","year":2020,"lang":"en","type":"article","venue":"Genome biology","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; McGill University Health Centre; McGill University and Génome Québec Innovation Centre; McGill University","funders":"Canadian Institutes of Health Research; Compute Canada","keywords":"Epigenomics; Biology; Human genetics; Genome Biology; Computational biology; Genome; Genomics; Graph; Evolutionary biology; Genetics; Computer science; Theoretical computer science; DNA methylation; Gene","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":[0.0002990185,0.0001891783,0.0002782306,0.00005728011,0.0000697088,0.00003008673,0.0005703111,0.0001902522,0.00004184156],"category_scores_gemma":[0.0000586558,0.00018714,0.00004882791,0.0001006661,0.0001705876,0.000003957994,0.0006407193,0.0001159449,0.00001134749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001507862,"about_ca_system_score_gemma":0.00009941862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001883826,"about_ca_topic_score_gemma":0.00002431659,"domain_scores_codex":[0.9984431,0.0001183217,0.0003291752,0.0007268407,0.00005041125,0.0003321823],"domain_scores_gemma":[0.9992464,0.00002161277,0.0001028988,0.0004531209,0.00002687793,0.0001490944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008626933,0.00001996055,0.01620833,0.0000231536,0.00005449394,0.000007668861,0.0002395672,0.00004128313,0.9738172,0.0001764039,0.000132947,0.009192748],"study_design_scores_gemma":[0.009142201,0.002891746,0.1199968,0.00003214594,0.0001720512,0.0002767558,0.001621959,0.01360549,0.01026349,0.009894248,0.8288354,0.003267636],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836243,0.01181117,0.002366264,0.001390908,0.00005922441,0.0001833656,0.0002747346,0.000009908626,0.0002800798],"genre_scores_gemma":[0.9928967,0.001127123,0.003220287,0.001105649,0.0002802156,0.000005810365,0.001257801,0.00002657852,0.0000797985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9635537,"threshold_uncertainty_score":0.7631347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02609817136561877,"score_gpt":0.2537285373061441,"score_spread":0.2276303659405253,"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."}}