Name
Abstract | ||
|---|---|---|
Superior accuracy achieved on different benchmark datasets, including both in silico and in vivo networks, shows that PLSNET reaches state-of-the-art performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s12859-016-1398-6 | BMC Bioinformatics |
Keywords | Field | DocType |
Ensemble,Gene Regulatory Network inference,Gene expression data,Partial least squares (PLS) | Least squares,Data mining,Feature selection,Computer science,Partial least squares regression,Artificial intelligence,Microarray gene expression,In silico,Inference,Bioinformatics,Gene regulatory network,DNA microarray,Machine learning | Journal |
Volume | Issue | ISSN |
17 | 1 | 1471-2105 |
Citations | PageRank | References |
2 | 0.38 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shun Guo | 1 | 7 | 1.52 |
| Qingshan Jiang | 2 | 588 | 77.27 |
| Lifei Chen | 3 | 40 | 3.30 |
| Donghui Guo | 4 | 107 | 21.93 |