{"id":"W2798407167","doi":"10.1101/312025","title":"Melissa: Bayesian clustering and imputation of single cell methylomes","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Engineering and Physical Sciences Research Council; Medical Research Council; University of Edinburgh","keywords":"Imputation (statistics); Methylation; Computational biology; Cluster analysis; DNA methylation; CpG site; Epigenetics; Biology; Computer science; Artificial intelligence; Missing data; Gene; Genetics; Gene expression; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005871873,0.001150351,0.002209823,0.001838009,0.00143385,0.00231124,0.004285838,0.002430965,0.009175547],"category_scores_gemma":[0.01994931,0.001373805,0.002259171,0.001995998,0.0009250142,0.001289884,0.002197998,0.003004834,0.003737884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001009551,"about_ca_system_score_gemma":0.001745715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007889204,"about_ca_topic_score_gemma":0.01261791,"domain_scores_codex":[0.9980019,0.001034069,0.00007956338,0.0005339929,0.0002492615,0.0001012244],"domain_scores_gemma":[0.9940761,0.003336568,0.000311772,0.001467444,0.0005812836,0.0002268352],"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":[0.001739099,0.0002681611,0.01807165,0.00106146,0.002092534,0.0006073233,0.0005862636,0.6300621,0.01623153,0.04099332,0.05484214,0.2334444],"study_design_scores_gemma":[0.0001190917,0.00004707633,0.001317044,0.0000425264,0.00005787744,0.00008342821,0.00003205142,0.9564154,0.004557868,0.02942439,0.007849671,0.00005353268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01693635,0.0006510488,0.9625905,0.0004739319,0.0001410189,0.0001260533,0.005346327,0.01279181,0.0009430745],"genre_scores_gemma":[0.169532,0.000325216,0.8023894,0.0005165215,0.0001614877,0.0006529537,0.01895513,0.003181462,0.004285838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009175547,"threshold_uncertainty_score":0.03105384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092483798528633,"score_gpt":0.2275879188943264,"score_spread":0.2166630809090401,"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."}}