Name
Title | ||
|---|---|---|
A novel protein complex identification algorithm based on gene co-expression (PCIA-GeCo) |
Abstract | ||
|---|---|---|
Recent studies have shown that protein complex is composed of core and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge(PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BIBM.2013.6732523 | BIBM |
Keywords | Field | DocType |
protein-protein interaction network,biological network,gene co-express,core proteins,pcia-geco,weighted ppi network,genetics,proteins,edge angle,molecular biophysics,molecular configurations,protein complex identification algorithm based on gene co-expression,attachment proteins,macromolecules,protein complex,gene expression data,f-measure,bioinformatics,coverage rate | Gene,Computer science,Macromolecule,Algorithm,Gene expression,Molecular biophysics,Bioinformatics,Protein function prediction | Conference |
Volume | Issue | ISSN |
null | null | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junmin Zhao | 1 | 7 | 3.97 |
| Xiaohua Hu | 2 | 2819 | 314.15 |
| Tingting He | 3 | 348 | 61.04 |
| Peng Li | 4 | 1 | 1.06 |
| Ming Zhang | 5 | 89 | 18.62 |
| Xianjun Shen | 6 | 24 | 12.95 |