{"id":"W2169628276","doi":"10.1093/nar/gkn735","title":"Optimization of experimental design parameters for high-throughput chromatin immunoprecipitation studies","year":2008,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"National Cancer Institute; Canadian Institutes of Health Research; Terry Fox Foundation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Pennsylvania","keywords":"Chromatin immunoprecipitation; Biology; ChIP-sequencing; ChIP-on-chip; DNA microarray; Computational biology; Chip; Immunoprecipitation; Chromatin; Microarray; Tiling array; Throughput; Gene chip analysis; Molecular biology; Genetics; Computer science; DNA; Gene expression; Gene; Promoter; Telecommunications; Chromatin remodeling","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005081741,0.0001104094,0.0001655011,0.00007324514,0.0002249256,0.00001308427,0.0002088957,0.0001108493,0.00001156427],"category_scores_gemma":[0.000250516,0.0001086815,0.00005988377,0.000120837,0.0002924552,0.000007331857,0.0001303982,0.00006893664,0.000003858564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006077328,"about_ca_system_score_gemma":0.0000897231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001620855,"about_ca_topic_score_gemma":0.000001120111,"domain_scores_codex":[0.9988407,0.0001396208,0.000256283,0.0002690785,0.0002383578,0.0002559158],"domain_scores_gemma":[0.9991333,0.0000883368,0.00008238065,0.0002898568,0.000368078,0.00003806517],"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":[0.0002122206,0.0001325327,0.00009680777,0.00003966682,0.0001209163,9.883304e-7,0.0009809764,0.03064523,0.9650964,0.0001969909,0.002009066,0.0004681917],"study_design_scores_gemma":[0.001179735,0.001877407,0.0004774154,0.00002374434,0.000009182881,0.000009703356,0.001844118,0.02478109,0.9690871,0.0003643355,0.0001633102,0.0001828818],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9435806,0.001103824,0.05454123,0.00005218674,0.00008460695,0.0005275611,0.00001686386,0.000008041581,0.00008512482],"genre_scores_gemma":[0.834933,0.0006592375,0.1639308,0.00001455923,0.00004615429,0.0001165966,0.0001016908,0.00002643963,0.0001715233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1093896,"threshold_uncertainty_score":0.4431902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08066271238926097,"score_gpt":0.355409762727437,"score_spread":0.274747050338176,"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."}}