Name
Abstract | ||
|---|---|---|
A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TCBB.2011.79 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
pmd factor,class indicator,conventional method,proposed pmd-based method,molecular pattern discovery,nonnegative matrix factorization,gene expression data,penalized matrix decomposition,cancer class discovery,complex class,hierarchical clustering,computational complexity,bioinformatics,cancer,lab on a chip,dna,affinity propagation,genetics,molecular biophysics,indexation,gene expression,matrix decomposition,complexity class,developmental biology,computational biology | Data mining,Computer science,Artificial intelligence,Cluster analysis,Hierarchical clustering,Affinity propagation,Matrix decomposition,Determining the number of clusters in a data set,Molecular biophysics,Non-negative matrix factorization,Bioinformatics,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
8 | 6 | 1557-9964 |
Citations | PageRank | References |
33 | 1.31 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-hou Zheng | 1 | 732 | 71.79 |
| Lei Zhang | 2 | 16326 | 543.99 |
| Vincent To-Yee Ng | 3 | 43 | 3.18 |
| Simon Chi Keung Shiu | 4 | 667 | 35.42 |
| De-Shuang Huang | 5 | 5532 | 357.50 |