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Genetic feature selection algorithm as an efficient glioma grade classifier.

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  • Author(s): Lin TH;Lin TH; Lin HY; Lin HY
  • Source:
    Scientific reports [Sci Rep] 2025 May 03; Vol. 15 (1), pp. 15497. Date of Electronic Publication: 2025 May 03.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Glioma*/genetics ; Glioma*/pathology ; Glioma*/classification ; Algorithms* ; Brain Neoplasms*/genetics ; Brain Neoplasms*/pathology ; Brain Neoplasms*/classification; Humans ; Neoplasm Grading ; Oligonucleotide Array Sequence Analysis ; Computational Biology/methods ; Gene Expression Profiling
    • Abstract:
      Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
      Gliomas are among the most lethal and debilitating cancers. Genetic testing is a rapidly evolving modality for cancer management. The advent of DNA microarrays enabled the utility of computational analyses in such management on a molecular basis. However, as current computational analyses remain insensitive to interactions between molecular features, they rarely postulate reasonable pathogenesis. The current study proposes a heuristic feature selection algorithm that identifies subsets of genes to almost perfectly classify glioma grades. The discretization technique in our method is a powerful tool against the tremendous data volume in DNA microarray. Instead of recognizing individual genetic features, the proposed algorithm helps identify specific gene subsets that play important roles in the pathogenesis of glioma.
      (© 2025. The Author(s).)
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    • Contributed Indexing:
      Keywords: Classification efficiency; Computational analysis; Discretization; Gene selection; Genetic testing; Glioma; Heuristic feature selection; Molecular analysis
    • Publication Date:
      Date Created: 20250503 Date Completed: 20250504 Latest Revision: 20250507
    • Publication Date:
      20250507
    • Accession Number:
      PMC12049469
    • Accession Number:
      10.1038/s41598-024-83879-2
    • Accession Number:
      40319095