{"id":"W2396709498","doi":"10.1007/978-1-62703-514-9_12","title":"Statistical Analysis of ChIP-seq Data with MOSAiCS","year":2013,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Human Genome Research Institute; National Institutes of Health","keywords":"Bioconductor; Preprocessor; Chromatin immunoprecipitation; Epigenomics; Computer science; Chip; Computational biology; R package; Protocol (science); Data mining; Biology; Artificial intelligence; Genetics; Gene; DNA methylation; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03178301,0.002233598,0.00556909,0.005793302,0.001886335,0.003212417,0.004330474,0.001840035,0.02629429],"category_scores_gemma":[0.0946684,0.001718906,0.003561695,0.007657833,0.00284301,0.001958489,0.003363696,0.005866893,0.004332392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273534,"about_ca_system_score_gemma":0.002866383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003711293,"about_ca_topic_score_gemma":0.00281151,"domain_scores_codex":[0.9748397,0.0105703,0.00235714,0.006621321,0.004300653,0.001310835],"domain_scores_gemma":[0.9405778,0.04578406,0.002304767,0.007483212,0.003088892,0.0007613722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01631632,0.002041236,0.1099368,0.01669119,0.02142562,0.003967533,0.006467922,0.125578,0.1137,0.07306283,0.2094521,0.3013605],"study_design_scores_gemma":[0.001715853,0.002654194,0.1043964,0.0009262497,0.003953357,0.001319205,0.001426998,0.554112,0.0871228,0.0862959,0.1552183,0.0008587623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07668499,0.0007076305,0.8043535,0.0006738153,0.0009913483,0.003750379,0.06683234,0.04277462,0.003231314],"genre_scores_gemma":[0.2227153,0.0002909988,0.7054406,0.0006803478,0.0002392846,0.02638881,0.0258435,0.01510763,0.003293509],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03178301,"threshold_uncertainty_score":0.1680866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685611684386683,"score_gpt":0.3634612087268056,"score_spread":0.3466050918829388,"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."}}