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Cancer is one of the most serious and harmful diseases that threatens humanity. Currently there is no robust treatment which leads to guaranteed cure from cancer. Thus, researchers from various domains are still working hard to identify molecules such as genes and proteins which could be handled and targeted as cancer biomarkers. Various methods have been developed and the research spans wide range of techniques from wet lab testing by biologists to computational methods by computer scientists. The latter research is promising because it greatly reduces the number of molecules as potential biomarkers. This project investigated existing literature data by integrating text mining, as well as gene–gene interactions. Different genes are highlighted in relationship to Glioma and temozolomide.

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Predicting the Role of Temozolomide Drug in Glioma by Integrating Available Genomic Databases and Computational Methods

  • 会议论文

  • 发表日期：

    2026年

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  • Aya Alhajj,
  • Zehra Bayindir,
  • Sleiman Alhajj,
  • Lama Alhajj,
  • Kashfia Sailunaz,
  • Mehmet Kaya,
  • Reda Alhajj

摘要

Cancer is one of the most serious and harmful diseases that threatens humanity. Currently there is no robust treatment which leads to guaranteed cure from cancer. Thus, researchers from various domains are still working hard to identify molecules such as genes and proteins which could be handled and targeted as cancer biomarkers. Various methods have been developed and the research spans wide range of techniques from wet lab testing by biologists to computational methods by computer scientists. The latter research is promising because it greatly reduces the number of molecules as potential biomarkers. This project investigated existing literature data by integrating text mining, as well as gene–gene interactions. Different genes are highlighted in relationship to Glioma and temozolomide.