Work

Molecular Analysis based on Genetic Feature Selection for Gliomas Classification

Year: 2024

Type: conference-paper

Abstract: Genetic testing is a rapidly evolving modality in cancer management, with DNA microarrays facilitating computational analyses at the molecular level. However, current computational methods often fail to account for gene interactions, limiting their ability to elucidate the underlying pathogenesis. This study introduces a heuristic feature selection algorithm that effectively categorizes glioma grades by identifying specific gene subsets. Our discretization technique addresses the challenge of large-scale microarray data, and by focusing on gene combinations rather than individual features, the algorithm provides insights into the critical roles of these subsets in glioma pathogenesis.

Source: International Conference on Computational Biology and Bioinformatics (ICCBB)

DOI: 10.1145/3715020.3715057

Authors: Ting-Han Lin, Hung-Yi Lin, Yu-Kang Chang

Institutions: China Medical University, China Medical University Hospital, National Taichung University of Science and Technology, Tungs' Taichung MetroHarbor Hospital

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

OpenAlex ID: W4411028287

FWCI:

Cites: 12

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Related to: 10