{"id":"W1891158457","doi":"10.1093/bioinformatics/btv615","title":"funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Child and Family Research Institute; University of British Columbia; Douglas Mental Health University Institute; McGill University; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université de Sherbrooke; McGill University Health Centre; Canadian Institute for Advanced Research; Jewish General Hospital","funders":"Canadian Institutes of Health Research","keywords":"Normalization (sociology); DNA methylation; DNA; Computer science; Computational biology; Methylation; R package; Biology; Genetics; Programming language; Gene; 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":[0.0004156251,0.0001217375,0.0001396648,0.00004368885,0.00004611093,0.00003156651,0.00028096,0.0001528609,0.00001007019],"category_scores_gemma":[0.0003026333,0.0001002009,0.00002499235,0.00006578734,0.00002766126,0.00004468927,0.0001163913,0.00002930081,0.000006835795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001054157,"about_ca_system_score_gemma":0.00009739661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001946001,"about_ca_topic_score_gemma":0.0001903068,"domain_scores_codex":[0.99917,0.0000346711,0.0003612204,0.0001472846,0.0001454309,0.0001413723],"domain_scores_gemma":[0.9986495,0.00002508745,0.0003236022,0.0006478971,0.0002659775,0.00008792985],"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":[0.001213504,0.0006292457,0.02041831,0.001473834,0.0001857183,0.000001362237,0.006117299,0.003971347,0.8525524,0.0002461382,0.01194085,0.10125],"study_design_scores_gemma":[0.001151397,0.0007476236,0.0009733815,0.00001656496,0.00005164324,8.05096e-7,0.0009097485,0.04289264,0.873118,0.0002169267,0.07967701,0.0002441918],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5025077,0.001301925,0.492138,0.00005231133,0.000281175,0.0008219462,0.000836246,0.00002762265,0.00203308],"genre_scores_gemma":[0.9248309,0.0001611575,0.06868464,0.00004795507,0.0001778066,0.00001295409,0.005613331,0.00002590895,0.0004453284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4234534,"threshold_uncertainty_score":0.4086075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07610816683406356,"score_gpt":0.3102720229849514,"score_spread":0.2341638561508879,"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."}}