{"id":"W2582660596","doi":"10.1101/095463","title":"Umap and Bismap: quantifying genome and methylome mappability","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Princess Margaret Cancer Centre; University of Toronto","funders":"University of Toronto; Princess Margaret Cancer Foundation; Natural Sciences and Engineering Research Council of Canada; University Health Network","keywords":"Genome; Bisulfite sequencing; Computational biology; Genetics; Biology; DNA sequencing; Genomics; Whole genome sequencing; Bisulfite; Hybrid genome assembly; Human genome; Reference genome; Cancer genome sequencing; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008809367,0.0004585316,0.0004226572,0.0001341085,0.0001523982,0.0001485311,0.0002516001,0.0006747413,0.00001021724],"category_scores_gemma":[0.0002543369,0.0004330106,0.00009621516,0.0001023911,0.0002201458,0.000008727156,0.0008472827,0.0002870703,0.000007017481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004530761,"about_ca_system_score_gemma":0.0001854814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001846714,"about_ca_topic_score_gemma":0.000003369215,"domain_scores_codex":[0.9975697,0.0001725934,0.0004447449,0.001200381,0.0001972181,0.0004153105],"domain_scores_gemma":[0.9981133,0.00004107213,0.0002784436,0.001037015,0.000263606,0.0002664993],"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":[0.00002295151,0.00003023597,0.01982925,0.0002383907,0.0000898169,0.00000399607,0.000005024477,0.000003867158,0.9795759,0.000178562,0.000008161648,0.00001381311],"study_design_scores_gemma":[0.0004500023,0.00009760587,0.2557103,0.00009522132,0.00007153075,1.410494e-8,0.000001978867,0.00002807037,0.7330415,0.00004859526,0.00975728,0.0006978809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770995,0.01597312,0.005600255,0.0002445505,0.0003566032,0.0004518645,0.0002039845,0.00005542197,0.0000146914],"genre_scores_gemma":[0.9916321,0.003812591,0.003883197,0.00006721146,0.0004364439,0.00006959989,0.000001411485,0.00008754258,0.000009920615],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2465344,"threshold_uncertainty_score":0.9998122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074881616749766,"score_gpt":0.2488106229403132,"score_spread":0.2280618067728155,"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."}}