{"id":"W3081341272","doi":"10.1007/978-1-0716-0664-3_8","title":"Profiling Chromatin Landscape at High Resolution and Throughput with 2C-ChIP","year":2020,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Chromatin immunoprecipitation; Chip; Computational biology; DNA sequencing; Chromatin; Chromosome conformation capture; Genome; ChIP-sequencing; Profiling (computer programming); Computer science; Throughput; Biology; Genetics; DNA; Gene; Chromatin remodeling; Gene expression; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001033833,0.0007496275,0.001239598,0.001248708,0.0009943334,0.001761632,0.001263198,0.001039027,0.003611322],"category_scores_gemma":[0.0009874377,0.0009332249,0.0007582573,0.001178229,0.0008319705,0.0008288239,0.0009672955,0.002226499,0.00241721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007275191,"about_ca_system_score_gemma":0.0007406994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003039363,"about_ca_topic_score_gemma":0.01168891,"domain_scores_codex":[0.9985701,0.0001944376,0.00004711695,0.0004097319,0.0005286098,0.0002498998],"domain_scores_gemma":[0.9993281,0.000291062,0.00003761027,0.0001524728,0.0001318508,0.00005882303],"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":[0.00004420357,0.00003506903,0.0004909358,0.00005288538,0.0000305678,0.00001667626,0.00002198961,0.0004635241,0.9943975,0.0003927318,0.0007927793,0.003261029],"study_design_scores_gemma":[0.00003695943,0.00008013877,0.01497154,0.00001415743,0.00005644439,0.0001614812,0.00003951653,0.01611797,0.9579309,0.001388655,0.009123669,0.00007850531],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3768678,0.006672104,0.5802758,0.002080315,0.0004780593,0.0006641254,0.01467124,0.006493408,0.0117972],"genre_scores_gemma":[0.53445,0.003878867,0.4253225,0.002268044,0.0003214053,0.001978914,0.01681116,0.001927139,0.01304202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003611322,"threshold_uncertainty_score":0.01208109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130245500378284,"score_gpt":0.3039487819819603,"score_spread":0.290924231944132,"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."}}