{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004344777,0.0002023129,0.000220361,0.0001774906,0.0004311035,0.0002006567,0.0006126542,0.0000527247,0.000008192735],"category_scores_gemma":[0.0001513738,0.0002034717,0.000162518,0.0005388109,0.000315603,0.00002722563,0.00145607,0.0001089694,0.0000216214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001034789,"about_ca_system_score_gemma":0.0002664239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004781421,"about_ca_topic_score_gemma":0.00000336513,"domain_scores_codex":[0.998359,0.00004709979,0.0004067346,0.0006320305,0.0002414553,0.0003137175],"domain_scores_gemma":[0.9992071,0.00003088171,0.0001346415,0.0003786231,0.0001446836,0.0001040815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006281804,0.0001382605,0.0007210361,0.0001524952,0.00006878006,0.00003170871,0.0001969321,0.04178302,0.8368768,0.06742485,0.004854844,0.04768845],"study_design_scores_gemma":[0.004527656,0.002107213,0.0235485,0.009333581,0.0004291635,0.0005978322,0.0007039059,0.08757997,0.2099878,0.4742558,0.1820037,0.004924921],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5041503,0.01332354,0.4752565,0.001239861,0.0008507423,0.0005829175,0.00003796789,0.0000280229,0.004530126],"genre_scores_gemma":[0.9586934,0.0004069789,0.03941397,0.001127545,0.000116101,0.00005699371,0.00008541712,0.00001710457,0.00008247398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6268891,"threshold_uncertainty_score":0.8297335,"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."}}