Cluster Ensemble Algorithm Based on Dual Neural Gas Applied to Cancer Gene Expression Profiles_Journal of Biomedical Engineering

Authors：

ZHANGXiaodong ¹ , CHENHantao ²

1. Department of Information, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China;
2. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China;

Corresponding author：

Keywords：

cluster ensemble; neural gas algorithm; gene expression profile; cancer diagnosis

DOI：

10.7507/1001-5515.20150017

Video：

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The microarray technology used in biological and medical research provides a new idea for the diagnosis and treatment of cancer. To find different types of cancer and to classify the cancer samples accurately, we propose a new cluster ensemble framework Dual Neural Gas Cluster Ensemble (DNGCE), which is based on neural gas algorithm, to discover the underlying structure of noisy cancer gene expression profiles. This framework DNGCE applies the neural gas algorithm to perform clustering not only on the sample dimension, but also on the attribute dimension. It also adopts the normalized cut algorithm to partition off the consensus matrix constructed from multiple clustering solutions. We obtained the final accurate results. Experiments on cancer gene expression profiles illustrated that the proposed approach could achieve good performance, as it outperforms the single clustering algorithms and most of the existing approaches in the process of clustering gene expression profiles.

Citation： ZHANGXiaodong, CHENHantao. Cluster Ensemble Algorithm Based on Dual Neural Gas Applied to Cancer Gene Expression Profiles. Journal of Biomedical Engineering, 2015, 32(1): 93-98. doi: 10.7507/1001-5515.20150017 Copy

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1. GOLUB T R, SLONIM D K, TAMAYO P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring[J]. Science, 1999, 286(5439): 531-537.
2. ZHAO Yuhai, YU J X, WANG Guoren, et al. Maximal subspace coregulated gene clustering[J]. IEEE Trans Knowl Data Eng, 2008, 20(1): 83-98.
3. WIGLE D A, JURISICA I, RADULOVICH N, et al. Molecular profiling of non-small cell lung cancer and correlation with disease-free survival[J]. Cancer Res, 2002, 62(11): 3005-3008.
4. 袁远, 季星来, 孙之荣, 等.Isomap在基因表达谱数据聚类分析中的应用[J].清华大学学报:自然科学版, 2004, 44(9):1286-1289.
5. 朱思峰, 刘芳, 柴争义.免疫聚类算法在基因表达数据分析中的应用[J].北京邮电大学学报, 2010, 33(2):54-57.
6. 陈军, 唐世星, 张吉强, 等.一种改进的FCM算法对海马组织基因的聚类分析[J].中国卫生统计, 2010, 27(4):338-340, 344.
7. 李俊林, 符红光.仿分子动理学数据聚类法在基因表达数据上的应用[J].计算机应用, 2011, 31(10):2774-2777.
8. 颜文胜.基于弹簧模型的基因表达数据可视化聚类[J].计算机工程, 2011, 37(5):202-203, 206.
9. 葛芳, 王年, 郭秀丽.一种改进的谱聚类算法及其在基因表达谱分析中的应用[J].安徽大学学报:自然科学版, 2012, 36(5):67-72.
10. 张林, 刘辉.基于Dirichlet过程无限混合模型的基因表达数据聚类算法[J].统计与决策, 2012, 352(4):27-29.
11. DUDOIT S, FRIDLYAND J. A prediction-based resampling method for estimating the number of clusters in a dataset[J]. Genome Biol, 2002, 3(7): 0036.1-0036.21.
12. SMOLKIN M, GHOSH D. Cluster stability scores for microarray data in cancer studies[J]. BMC Bioinformatics, 2003, 4: 36.
13. MONTI S, TAMAYO P, MESIROV J, et al. Consensus clustering: a resamlping-based method for class discovery and visualization of gene expression microarray data[J]. Mach Learn, 2003, 52(1-2): 91-118.
14. HANDL J, KNOWLES J, KELL D B. Computational cluster validation in post-genomic data analysis[J]. Bioinformatics, 2005, 21(15): 3201-3212.
15. BERTONI A, VALENTINI G. Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses[J]. Artif Intell Med, 2006, 37(2): 85-109.
16. YU Zhiwen, WONG H S, WANG Hongqiang. Graph-based consensus clustering for class discovery from gene expression data[J]. Bioinformatics, 2007, 23(21): 2888-2896.
17. YU Zhiwen, WONG H S. Class discovery from gene expression data based on perturbation and cluster ensemble[J]. IEEE Trans Nanobioscience, 2009, 8(2): 147-160.
18. SU A I, COOKE M P, CHING K A, et al. Large-scale analysis of the human and mouse transcriptomes[J]. Proc Natl Acad Sci U S A, 2002, 99(7): 4465-4470.
19. BHATTACHARJEE A, RICHARDS W G, STAUNTON J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses[J]. Proc Natl Acad Sci U S A, 2001, 98(24): 13790-13795.
20. RAMASWAMY S, TAMAYO P, RIFKIN R, et al. Multiclass cancer diagnosis using tumor gene expression signatures[J]. Proc Natl Acad Sci, 2001, 98(26): 15149-15154.

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