Name
Title | ||
|---|---|---|
Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions |
Abstract | ||
|---|---|---|
Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than tumor metastasis itself, to avoid conf... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCBB.2020.2992605 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | DocType | Volume |
Entropy,Tumors,Metastasis,Symmetric matrices,Gene expression,Feature extraction,Data models | Journal | 18 |
Issue | ISSN | Citations |
6 | 1545-5963 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yushan Qiu | 1 | 20 | 6.28 |
| Jiang Hao | 2 | 15 | 2.01 |
| Wai-Ki Ching | 3 | 683 | 78.66 |