{"id":"W4388752751","doi":"10.1101/2023.11.16.567416","title":"Simulating cell-free chromatin using preclinical models for cancer-specific biomarker discovery","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; University of Toronto; Princess Margaret Cancer Foundation","keywords":"Chromatin; Nucleosome; Histone; Epigenetics; Biology; Computational biology; Scaffold/matrix attachment region; Chromatin remodeling; Bivalent chromatin; ChIP-sequencing; Heterochromatin; Chromatin immunoprecipitation; ChIP-on-chip; Histone code; Epigenomics; ChIA-PET; Cell biology; Genetics; DNA; Gene expression; Gene; DNA methylation; Promoter","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":[0.0004185374,0.0003892873,0.0002923596,0.0002426989,0.0001531131,0.0004343801,0.0006269275,0.0006733066,0.001009924],"category_scores_gemma":[0.00094129,0.0002410256,0.0004841134,0.0001883445,0.00037757,0.0002867698,0.0003993956,0.0005065082,0.0001905403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005516244,"about_ca_system_score_gemma":0.0004981421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003057632,"about_ca_topic_score_gemma":0.002365798,"domain_scores_codex":[0.9998573,0.00003948175,0.000006854353,0.0000351367,0.00004500558,0.00001604341],"domain_scores_gemma":[0.9996265,0.0002317059,0.00004031551,0.00003916056,0.00004007822,0.00002229847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005362293,0.00004729478,0.001382786,0.00007324663,0.0000193301,0.0000713075,0.00002843044,0.9074855,0.08563913,0.00228301,0.000117421,0.0027989],"study_design_scores_gemma":[0.00001169737,0.00006726418,0.0003498789,0.000004413155,0.000008671717,0.00002155405,0.00001140558,0.9732815,0.02443987,0.0009382302,0.0008563712,0.000009196682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3952547,0.0005452061,0.5973551,0.0002288053,0.00009247137,0.0001931103,0.001131397,0.0006697301,0.00452945],"genre_scores_gemma":[0.9173977,0.0004133771,0.07986699,0.00007624839,0.00001372338,0.0002262859,0.0006200828,0.00009019927,0.001295518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003057632,"threshold_uncertainty_score":0.006079674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04726643981864629,"score_gpt":0.279076391211501,"score_spread":0.2318099513928547,"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."}}