Name
Title | ||
|---|---|---|
Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization |
Abstract | ||
|---|---|---|
Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncRNAs on a large scale. However, many models did not consider the correlation information among multiple subcellular localizations of ncRNAs. This study proposes a radial basis function neural network based on shared subspace learning (RBFNN-SSL), which extract shared structures in multi-labels. To evaluate performance, our classifier is tested on three ncRNA datasets. Our model achieves better performance in experimental results. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neunet.2022.09.026 | Neural Networks |
Keywords | DocType | Volume |
Biological sequence classification,Shared subspace learning,Radial basis function neural networks,Multi-label classification | Journal | 156 |
Issue | ISSN | Citations |
1 | 0893-6080 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yihong ding | 1 | 40 | 10.39 |
| Prayag Tiwari | 2 | 43 | 15.01 |
| Fei Guo | 3 | 42 | 10.37 |
| quan zou | 4 | 558 | 67.61 |