Name
Abstract | ||
|---|---|---|
Microarray technology enables the collection of vast amounts of gene expression data from biological experiments. Clustering algorithms have been successfully applied to exploring the gene expression data. Since a set of genes may be possible correlated to a subset of samples, it is useful to use co-clustering to recover co-clusters in the gene expression data. In this paper, we propose a novel algorithm, called Subspace Weighting Co-Clustering (SWCC), for high dimensional gene expression data. In SWCC, a gene subspace weight matrix is introduced to identify the contribution of gene objects in distinguishing different sample clusters. We design a new co-clustering objective function to recover the co-clusters in the gene expression data, in which the subspace weight matrix is employed. An iterative algorithm is developed to solve the objective function, in which the subspace weight matrix is automatically computed during the iterative co-clustering process. Our empirical study shows encouraging results of the proposed algorithm in comparison with six state-of-the-art clustering algorithms on ten gene expression data sets. We also propose to use SWCC for gene clustering and selection. The experimental results show that the selected genes can improve the classification performance of Random Forests. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCBB.2017.2705686 | IEEE/ACM transactions on computational biology and bioinformatics |
Keywords | Field | DocType |
Clustering algorithms,Gene expression,Partitioning algorithms,Algorithm design and analysis,Linear programming,Entropy,Approximation algorithms | Data mining,Data set,Weighting,Computer science,Artificial intelligence,Biclustering,Cluster analysis,Random forest,Subspace topology,Correlation clustering,Iterative method,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 2 | 1557-9964 |
Citations | PageRank | References |
4 | 0.42 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojun Chen | 1 | 1298 | 107.51 |
| Joshua Zhexue Huang | 2 | 20 | 4.39 |
| Wu Qingyao | 3 | 23 | 1.65 |
| Min Yang | 4 | 155 | 41.56 |