ArticleBiology direct2021
A novel gene selection method for gene expression data for the task of cancer type classification.
Article in Biology direct, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A hybrid feature extraction framework combining PCA and mutual information for gene expression based lung cancer classification.PloS one · 2026Article
- A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.PloS one · 2023Review
- Alpha-actnin-4 (ACTN4) selectively affects the DNA double-strand breaks repair in non-small lung carcinoma cells.Biology direct · 2022Article
- p53 regulates expression of nuclear envelope components in cancer cells.Biology direct · 2022Article
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Authors and funding
3 authors.
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Abstract
Cancer is a poligenetic disease with each cancer type having a different mutation profile. Genomic data can be utilized to detect these profiles and to diagnose and differentiate cancer types. Variant calling provide mutation information. Gene expression data reveal the altered cell behaviour. The combination of the mutation and expression information can lead to accurate discrimination of different cancer types. In this study, we utilized and transferred the information of existing mutations for a novel gene selection method for gene expression data. We tested the proposed method in order to diagnose and differentiate cancer types. It is a disease specific method as both the mutations and expressions are filtered according to the selected cancer types. Our experiment results show that the proposed gene selection method leads to similar or improved performance metrics compared to classical feature selection methods and curated gene sets.
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