Name
Abstract | ||
|---|---|---|
The proposed PU learning approach achieved a competitive predictive performance compared with currently available methods. This PU learning schema may also be effectively employed and applied to address the prediction problems of other important types of protein PTM site and functional sites. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12859-019-2700-1 | BMC bioinformatics |
Keywords | Field | DocType |
AlphaMax,Positive unlabelled-learning,Protein glycosylation prediction,Sequence analysis,Sequence-derived features,Supervised-learning | Human proteome project,Glycosylation,Protein stability,Biology,Metabolic pathway,Protein function,Genetics,DNA microarray,Model learning,Sequence analysis | Journal |
Volume | Issue | ISSN |
20 | 1 | 1471-2105 |
Citations | PageRank | References |
1 | 0.35 | 34 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuyi Li | 1 | 97 | 11.25 |
| Yang Zhang | 2 | 580 | 47.16 |
| Anthony W Purcell | 3 | 8 | 1.49 |
| Geoffrey I. Webb | 4 | 3130 | 234.10 |
| Kuo-Chen Chou | 5 | 946 | 64.26 |
| Trevor Lithgow | 6 | 15 | 3.16 |
| Chen Li | 7 | 68 | 6.46 |
| Jiangning Song | 8 | 374 | 41.93 |