Issue No. 02 - March/April (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.135
Shu-Lin Wang , Coll. of Inf. Sci. & Eng., Hunan Univ., Changsha, China
Yi-Hai Zhu , Intell. Comput. Lab., Hefei Inst. of Intell. Machines, Hefei, China
Wei Jia , Intell. Comput. Lab., Hefei Inst. of Intell. Machines, Hefei, China
De-Shuang Huang , Sch. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China
Tumor classification based on Gene Expression Profiles (GEPs), which is of great benefit to the accurate diagnosis and personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden in differentially expressed genes. Concretely, two correlation filters, i.e., Minimum Average Correlation Energy (MACE) and Optimal Tradeoff Synthetic Discriminant Function (OTSDF), are introduced to determine whether a test sample matches the templates synthesized for each subclass. The experiments on six publicly available data sets indicate that the proposed method is robust to noise, and can more effectively avoid the effects of dimensionality curse. Compared with many model-based methods, the correlation filter-based method can achieve better performance when balanced training sets are exploited to synthesize the templates. Particularly, the proposed method can detect the similarity of overall pattern while ignoring small mismatches between test sample and the synthesized template. And it performs well even if only a few training samples are available. More importantly, the experimental results can be visually represented, which is helpful for the further analysis of results.
Correlation, Tumors, Training, Noise, Feature extraction, Matched filters, Robustness
Shu-Lin Wang, Yi-Hai Zhu, Wei Jia and De-Shuang Huang, "Robust Classification Method of Tumor Subtype by Using Correlation Filters," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 580-591, 2012.