Name
Title | ||
|---|---|---|
Network analysis based on low-rank method for mining information on integrated data of multi-cancers. |
Abstract | ||
|---|---|---|
•RPCA was introduced into the gene expression data as a processing method to achieve data denoising and reconstruction.•Influence by low-rank characteristics of RPCA, network construction was carried out on the reconstructed integrated data of multi-cancers, and some common cancer-related clues were detected.•The RPCA-based denoising network mining model is reliable and efficient. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compbiolchem.2018.11.027 | Computational Biology and Chemistry |
Keywords | Field | DocType |
Noise reduction,Gene co-expression network,Multi-cancers,Integrated data,Abnormally expressed genes | Genome,Noise reduction,Data mining,Biology,Robust principal component analysis,Betweenness centrality,Network analysis,Genetics | Journal |
Volume | ISSN | Citations |
78 | 1476-9271 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mi-Xiao Hou | 1 | 3 | 1.76 |
| Gao Ying-Lian | 2 | 29 | 18.73 |
| Liu Jin-Xing | 3 | 40 | 16.11 |
| Ling-yun Dai | 4 | 5 | 5.85 |
| Xiang-Zhen Kong | 5 | 87 | 6.04 |
| Junliang Shang | 6 | 42 | 14.78 |