Name
Abstract | ||
|---|---|---|
We construct four dynamic PPI networks, and accurately predict many well-characterized protein complexes. The experimental results show that (i) the dynamic active information significantly improves the performance of protein complex prediction; (ii) our method can effectively make good use of both the dynamic active information and the topology structure information of dynamic PPI networks to achieve state-of-the-art protein complex prediction capabilities. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s12859-016-1101-y | BMC Bioinformatics |
Keywords | Field | DocType |
Positive Predictive Value, Predict Protein Complex, Protein Complex Prediction, Active Time Point, Integrate Gene Expression Data | Protein Interaction Map,Protein–protein interaction,Computer science,Transcriptome,Systems biology,Artificial intelligence,Bioinformatics,Computational biology,DNA microarray,Machine learning | Journal |
Volume | Issue | ISSN |
17 | S-7 | 1471-2105 |
Citations | PageRank | References |
5 | 0.40 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijia Zhang | 1 | 113 | 14.67 |
| Hongfei Lin | 2 | 768 | 122.52 |
| Zhihao Yang | 3 | 270 | 36.04 |
| Jian Wang | 4 | 112 | 18.98 |
| Yiwei Liu | 5 | 5 | 0.40 |
| Shengtian Sang | 6 | 14 | 2.95 |