{"id":"W2091244202","doi":"10.1186/1471-2105-13-199","title":"Normalization of ChIP-seq data with control","year":2012,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Cancer Institute; National Human Genome Research Institute; National Institutes of Health","keywords":"Normalization (sociology); Chip; Computer science; False discovery rate; DNA microarray; Data mining; Computational biology; Biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001955516,0.0001001568,0.0001114037,0.0000248267,0.00003407938,0.00001253326,0.0002732289,0.00007906983,0.000008162392],"category_scores_gemma":[0.0000343465,0.00007988211,0.00002384098,0.00005704777,0.00004737216,0.00001783636,0.0001247002,0.00003321205,0.000008795474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005354055,"about_ca_system_score_gemma":0.00006341764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003885381,"about_ca_topic_score_gemma":0.00002075806,"domain_scores_codex":[0.999366,0.00001249347,0.0002584154,0.00007203375,0.0001062759,0.0001848219],"domain_scores_gemma":[0.9990772,0.000007756002,0.0001872332,0.0006086734,0.00005728981,0.00006186636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007017403,0.0009091726,0.8748881,0.002200124,0.0007306294,7.114569e-7,0.002162503,0.0213396,0.06914473,0.007572854,0.009913508,0.01043635],"study_design_scores_gemma":[0.003961071,0.0006518401,0.05390304,0.00006461827,0.0002158213,0.0000772926,0.001050204,0.8995537,0.01694769,0.00004375335,0.02272281,0.0008081877],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4570264,0.0002370416,0.5394139,0.000014145,0.0001116531,0.0002266378,0.0002442969,0.00001080685,0.002715086],"genre_scores_gemma":[0.9097644,0.00004686031,0.08880959,0.0001050727,0.0000952621,0.000003418482,0.001098901,0.00001366905,0.0000628271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8782141,"threshold_uncertainty_score":0.3257498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01260049861669054,"score_gpt":0.2263756072074164,"score_spread":0.2137751085907258,"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."}}