{"id":"W4410778406","doi":"10.1002/wcms.70029","title":"Building Nucleosome Positioning Maps: Discovering Hidden Gems","year":2025,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Computational Molecular Science","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research; Ontario Institute for Cancer Research; University Health Network; University of Toronto; Princess Margaret Cancer Centre; Queen's University","funders":"Terry Fox Research Institute; Queen's University; Canada Research Chairs; Government of Ontario; Princess Margaret Cancer Foundation","keywords":"Nucleosome; Computer science; Computational biology; Biology; Chromatin; Genetics; DNA","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":[0.001090208,0.0007658187,0.0008816431,0.002745747,0.0006207065,0.00110438,0.00146346,0.0009149915,0.002196368],"category_scores_gemma":[0.005321501,0.0007084348,0.0009108824,0.002142797,0.0006134268,0.001339009,0.001544912,0.0008270429,0.001180663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004454947,"about_ca_system_score_gemma":0.0007023578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004080664,"about_ca_topic_score_gemma":0.003690662,"domain_scores_codex":[0.9995154,0.0001285105,0.0000266744,0.0001724397,0.000108632,0.00004825372],"domain_scores_gemma":[0.9980111,0.001228057,0.0001833625,0.0002500147,0.0002533914,0.00007398841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005824953,0.0001661525,0.0376117,0.0015667,0.000454552,0.0008904692,0.0009679882,0.4340613,0.02761369,0.0594158,0.01731255,0.4193566],"study_design_scores_gemma":[0.0000192908,0.00002515879,0.002216547,0.00006285929,0.00004449271,0.0001173296,0.0001129015,0.9473883,0.004605459,0.0406763,0.004708052,0.0000233371],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.08441085,0.001861638,0.9053676,0.0003096818,0.00007646662,0.00005881323,0.002875168,0.003163442,0.001876306],"genre_scores_gemma":[0.4761141,0.001270354,0.5109832,0.0001314887,0.0001024585,0.0001089316,0.009085434,0.000562777,0.00164128],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004080664,"threshold_uncertainty_score":0.008113801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00919458891717767,"score_gpt":0.3143739397996303,"score_spread":0.3051793508824526,"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."}}