Name
Abstract | ||
|---|---|---|
In recent decades, the availability of comprehensive genomic data has facilitated the insight of molecular portraits of cancer. Specifically, by conducting cancer clustering, cancer samples can be divided into several groups according to their differences and similarities in molecular characteristics. Traditional cancer clustering usually analyzes cancer samples from a single tissue, but such analysis cannot reveal the connections among different types of cancer. Landscape analysis across human cancers can help discover molecular signatures shared across cancer tissues, providing an opportunity to design new gene therapy tailored for different cancer patients. However, the noise level in genomic data is high. The robust clustering method is crucial to tackle this problem. In this paper, we propose a new robust clustering method to approach the landscape analysis for TCGA cancer data from a novel view, which is to eliminate the noise and then perform clustering on the cleaned data rather than weaken the effect of noise as existing noise-resistant norm methods. Extensive experiments on both genomic datasets and clustering benchmark datasets confirm the effectiveness and correctness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICDM.2016.0026 | 2016 IEEE 16th International Conference on Data Mining (ICDM) |
Keywords | Field | DocType |
Robust Clustering,Cancer Genome Landscapes,Denoised Clustering Model | Genome,Data mining,Computer science,Noise level,Correctness,Robustness (computer science),Genomics,Artificial intelligence,Landscape analysis,Cluster analysis,Machine learning,Cancer | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-1-5090-5474-9 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongchang Gao | 1 | 54 | 8.32 |
| Xiaoqian Wang | 2 | 335 | 16.72 |
| Heng Huang | 3 | 3080 | 203.21 |