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Double Selection based Semi-Supervised Clustering Ensemble for Tumor Clustering from Gene Expression Profiles
PrePrint
ISSN: 1545-5963
Tumor clustering is one of the important techniques for tumor discovery from cancer gene expression profiles, which is useful for the diagnosis and treatment of cancer. While different algorithms have been proposed for tumor clustering, few make use of the expert’s knowledge to better the performance of tumor discovery. In this paper, we first view the expert’s knowledge as constraints in the process of clustering, and propose a feature selection based semi-supervised cluster ensemble framework (FS-SSCE) for tumor clustering from biomolecular data. Compared with traditional tumor clustering approaches, the proposed framework FS-SSCE is featured by two properties: (1) The adoption of feature selection techniques to dispel the effect of noisy genes. (2) The employment of the binate constraint based K-means algorithm to take into account the effect of experts’ knowledge. Then, a double selection based semi-supervised cluster ensemble framework (DS-SSCE) which not only applies the feature selection technique to perform gene selection on the gene dimension, but also selects an optimal subset of representative clustering solutions in the ensemble and improve the performance of tumor clustering using the normalized cut algorithm. DS-SSCE also introduces a confidence factor into the process of constructing the consensus matrix by considering the prior knowledge of the dataset. Finally, we design a modified double selection based semi-supervised cluster ensemble framework (MDS-SSCE) which adopts multiple clustering solution selection strategies and an aggregated solution selection function to choose an optimal subset of clustering solutions. The results in the experiments on cancer gene expression profiles show that (i) FS-SSCE, DS-SSCE and MDS-SSCE are suitable for performing tumor clustering from bio-molecular data. (ii) MDSSSCE outperforms a number of state-of-the-art tumor clustering approaches on most of the datasets.
Citation:
Hongsheng Chen, Hau San Wong, Guoqiang Han, Le Li, Jane You, Jiming Liu, "Double Selection based Semi-Supervised Clustering Ensemble for Tumor Clustering from Gene Expression Profiles," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18 April 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TCBB.2014.2315996>
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