{"id":"W3036259867","doi":"10.1093/gigascience/giaa066","title":"CandiMeth: Powerful yet simple visualization and quantification of DNA methylation at candidate genes","year":2020,"lang":"en","type":"article","venue":"GigaScience","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; Biotechnology and Biological Sciences Research Council; Medical Research Council Canada; Public Health Agency; Medical Research Council; Cereal Partners Worldwide","keywords":"DNA methylation; Computational biology; Genome browser; Biology; Methylation; Workflow; Visualization; Genome; Epigenomics; Candidate gene; Epigenetics; Genomics; Computer science; Gene; Genetics; Data mining; Database; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002500547,0.00008084979,0.00009152415,0.00003233637,0.00008177116,0.00001820486,0.00009604965,0.00006324358,0.00001347528],"category_scores_gemma":[0.0001944477,0.00007913195,0.00002422411,0.0001975763,0.00009848597,0.000009121384,0.00007868913,0.00002047504,0.000003398826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008847208,"about_ca_system_score_gemma":0.00004460389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000511059,"about_ca_topic_score_gemma":0.00005911342,"domain_scores_codex":[0.9991843,0.00005636369,0.0001925148,0.0002986785,0.0001529614,0.000115173],"domain_scores_gemma":[0.9995093,0.0000156712,0.0001428606,0.0001518079,0.00009909353,0.00008124087],"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":[0.00002018523,0.000007305683,0.007980694,0.0000134773,0.000004270114,1.213029e-7,0.0001902153,0.0002831835,0.9886541,0.0002284203,0.00006003366,0.002557966],"study_design_scores_gemma":[0.0001461767,0.0001558961,0.01110706,0.000002780205,0.000009599823,5.310494e-7,0.00004492689,0.00377925,0.9787745,0.000132613,0.005753112,0.00009358567],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9563856,0.00133981,0.04178774,0.0001393628,0.00005796468,0.0001198829,0.00002134755,0.00000708568,0.0001411603],"genre_scores_gemma":[0.99875,0.0004893411,0.00041787,0.00008075238,0.00003662719,0.000002931433,0.0001623623,0.000008106565,0.00005199966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04236437,"threshold_uncertainty_score":0.3226908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02236607436095617,"score_gpt":0.2937630208027434,"score_spread":0.2713969464417872,"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."}}