| Autor: |
Ya-Wei Zhao, Zhen-Dong Su, Wuritu Yang, Hao Lin, Wei Chen, Hua Tang |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
|
| Zdroj: |
International Journal of Molecular Sciences, Vol 18, Iss 9, p 1838 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
1422-0067 |
| DOI: |
10.3390/ijms18091838 |
| Popis: |
Ion channels (IC) are ion-permeable protein pores located in the lipid membranes of all cells. Different ion channels have unique functions in different biological processes. Due to the rapid development of high-throughput mass spectrometry, proteomic data are rapidly accumulating and provide us an opportunity to systematically investigate and predict ion channels and their types. In this paper, we constructed a support vector machine (SVM)-based model to quickly predict ion channels and their types. By considering the residue sequence information and their physicochemical properties, a novel feature-extracted method which combined dipeptide composition with the physicochemical correlation between two residues was employed. A feature selection strategy was used to improve the performance of the model. Comparison results of in jackknife cross-validation demonstrated that our method was superior to other methods for predicting ion channels and their types. Based on the model, we built a web server called IonchanPred which can be freely accessed from http://lin.uestc.edu.cn/server/IonchanPredv2.0. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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