ArticleScientific reports2025
Genetic feature selection algorithm as an efficient glioma grade classifier.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
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2 authors.
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Abstract
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.
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