Name
Title | ||
|---|---|---|
Improving prediction of heterodimeric protein complexes using combination with pairwise kernel. |
Abstract | ||
|---|---|---|
We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2017-5 | BMC Bioinformatics |
Keywords | Field | DocType |
Combination kernel,Heterodimeric protein complex,Pairwise kernel | Kernel (linear algebra),Pairwise comparison,Protein Interaction Map,Protein multimerization,Protein domain,Biology,Support vector machine,Computational biology,Genetics,Phylogenetics,DNA microarray | Journal |
Volume | Issue | ISSN |
19-S | 1 | 1471-2105 |
Citations | PageRank | References |
2 | 0.37 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peiying Ruan | 1 | 22 | 2.02 |
| Morihiro Hayashida | 2 | 154 | 21.88 |
| Tatsuya Akutsu | 3 | 2169 | 216.05 |
| Jean-philippe Vert | 4 | 2754 | 158.52 |