Feature importance in within-patient subset experiments find H3K27Ac to be the most important feature for gene expression prediction.

Feature importance in within-patient subset experiments find H3K27Ac to be the most important feature for gene expression prediction.

<p>H3K27Ac is the most important feature by a large margin in within-patient testing for both GSC1 <b>(A)</b> and GSC2 <b>(B)</b>.</p> <p>(EPS)</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yusuke Suita (14381481) (author)
مؤلفون آخرون:	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)
منشور في:	2025
الموضوعات:	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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