Name
Title | ||
|---|---|---|
Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features |
Abstract | ||
|---|---|---|
Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1155/2022/7493834 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
DocType | Volume | ISSN |
Journal | 2022 | 1748-670X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mujiexin Liu | 1 | 0 | 0.34 |
| Hui Chen | 2 | 0 | 0.34 |
| Dong Gao | 3 | 0 | 0.34 |
| Cai-Yi Ma | 4 | 0 | 0.34 |
| Zhao-Yue Zhang | 5 | 8 | 1.84 |