Illustration of data preprocessing process.

<p>The epigenetic feature values per gene are extracted, formed into matrices, and standardized for model input. Each gene’s observed RNA-seq value is extracted and transformed creating the target variable for the model.</p> <p>(TIFF)</p>

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Bibliographic Details
Main Author:	Yusuke Suita (14381481) (author)
Other Authors:	Hardy Bright Jr (22008830) (author), Yuan Pu (3509042) (author), Merih Deniz Toruner (22008833) (author), Jordan Idehen (22008836) (author), Nikos Tapinos (152991) (author), Ritambhara Singh (689753) (author)
Published:	2025
Subjects:	Cell Biology Genetics Molecular Biology Developmental Biology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified providing valuable insights mechanisms dynamically regulate improving glioblastoma outcomes glioblastoma stem cells evade targeted therapies investigate different ml enhancer activity characterized predict gene expression gene expression provides one patient generalizes common distributional pattern combining epigenetic features h3k27ac peaks across glioblastoma </ p driving transcript expression using machine learning gscs ), contributing common epigenetic pattern gscs across patients ml ), transcript expression machine learning enhancer activation single patient patient prediction deep learning epigenetic drivers eradicating gscs enabling gscs various sources ultimately build tumor heterogeneity therapeutic resistance targeting strategies rnapii chip resistant mesenchymal remarkably similar phenotypic plasticity phenotypic heterogeneity model trained like phenotypes key oncogenic including therapy including atac high performance h3k27ac chip h3k27ac alone findings suggest environmental cues employ cross
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Illustration of data preprocessing process.

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