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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shuqin Wang Chunbao Zhou Yingsi Wu Jianxin Wang Chunguang Zhou Yanchun Liang |
| Copyright Year | 2008 |
| Abstract | Various researches have shown that machine learning approaches can be successfully used to detect and classify cancer tissue samples by their gene expression patterns. In this paper, an entropy-based improved k-TSP method (Ik-TSP) is proposed. We calculate the entropy for each gene based on the gene expression profile, and then find the best threshold of entropy depending on LOOCV accuracy for each gene expression dataset. Finally we select key genes for each gene expression dataset according to the best threshold and use them to implement Ik-TSP method to classify the cancer. Compared to 7 cancer classifiers mentioned in this paper in 9 binary public gene expression datasets of human cancers, the Ik-TSP method achieves an average LOOCV accuracy of 95.39%, and improves 3% better than the k-TSP method. Simulated experimental results show that the proposed Ik-TSP method is applicable to classify human cancers. |
| Starting Page | 976 |
| Ending Page | 981 |
| File Size | 249315 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769533988 |
| DOI | 10.1109/ICYCS.2008.215 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Humans Entropy Gene expression classifying cancers Computer science Support vector machines Accuracy Bayesian methods Support vector machine classification key gene k-TSP Machine learning gene expression profile Cancer |
| Content Type | Text |
| Resource Type | Article |
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