Name
Abstract | ||
|---|---|---|
Gene set enrichment (GSE) isa useful tool for analyzing and interpreting large molecular datasets generated by modern biomedical science. The accuracy and reproducibility of GSE analysis are heavily affected by the quality and integrity of gene sets annotations. In this paper, we propose a novel method, robust trace-norm multitask learning, to solve the optimization problem of gene set annotations... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCBB.2017.2690427 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | Field | DocType |
Optimization,Logistics,Robustness,Training data,Learning systems,Linear programming | Data mining,Computer science,Robustness (computer science),Regularization (mathematics),Linear programming,Artificial intelligence,Discriminative model,Optimization problem,Row,Multi-task learning,Annotation,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 3 | 1545-5963 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianpeng Liang | 1 | 7 | 1.46 |
| Lin Zhu | 2 | 74 | 4.93 |
| De-Shuang Huang | 3 | 5532 | 357.50 |