ArticleCancer informatics2010
A robust gene selection method for microarray-based cancer classification.
Article in Cancer informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer.Scientific reports · 2021Article
- Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data.Genes & genomics · 2019Article
- Lung cancer prediction from microarray data by gene expression programming.IET systems biology · 2016Article
- Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach.Current bioinformatics · 2014Article
- Microarray-based cancer prediction using single genes.BMC bioinformatics · 2011Article
- Rough set soft computing cancer classification and network: one stone, two birds.Cancer informatics · 2010Article
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2 authors.
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Abstract
Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.
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