{"id":"W1487374700","doi":"10.1002/9783527625130.ch15","title":"ChIP‐Seq: Mapping of Protein–DNA Interactions","year":2008,"lang":"en","type":"other","venue":"","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia","funders":"","keywords":"Sanger sequencing; DNA sequencing; Chip; Computational biology; Computer science; DNA; Biology; Genetics; Telecommunications","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":[0.000511665,0.001160095,0.0009701281,0.001369863,0.0005279976,0.001115714,0.001206225,0.0006857066,0.04436853],"category_scores_gemma":[0.0003928087,0.0006670274,0.0006948731,0.001410285,0.000295221,0.0006780176,0.0006577176,0.001486311,0.05133375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006209302,"about_ca_system_score_gemma":0.0007646774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001171047,"about_ca_topic_score_gemma":0.003703642,"domain_scores_codex":[0.9996055,0.00003731812,0.00001760714,0.00009511495,0.0002129877,0.00003158756],"domain_scores_gemma":[0.9998375,0.0000571136,0.00001050151,0.00002356786,0.00004927775,0.00002200187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001366422,0.00008589625,0.0004137894,0.001498274,0.00004863113,0.00009065818,0.00004148137,0.0008236809,0.2212235,0.005348727,0.4940314,0.2762573],"study_design_scores_gemma":[0.00001925622,0.00004114204,0.001405003,0.000114361,0.00003006057,0.0002832854,0.00001550961,0.001111132,0.08995586,0.002035012,0.904946,0.00004333539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01162044,0.09488583,0.4017364,0.005213159,0.007999762,0.001318983,0.1361885,0.03765349,0.3033835],"genre_scores_gemma":[0.0138724,0.07070914,0.3594661,0.005234766,0.001195265,0.001466667,0.1089801,0.006065964,0.4330096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04436853,"threshold_uncertainty_score":0.1484275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.016403343855041,"score_gpt":0.2553825976320558,"score_spread":0.2389792537770148,"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."}}