IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types
AbstractIon 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
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Zhao, Y.-W.; Su, Z.-D.; Yang, W.; Lin, H.; Chen, W.; Tang, H. IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types. Int. J. Mol. Sci. 2017, 18, 1838.
Zhao Y-W, Su Z-D, Yang W, Lin H, Chen W, Tang H. IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types. International Journal of Molecular Sciences. 2017; 18(9):1838.Chicago/Turabian Style
Zhao, Ya-Wei; Su, Zhen-Dong; Yang, Wuritu; Lin, Hao; Chen, Wei; Tang, Hua. 2017. "IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types." Int. J. Mol. Sci. 18, no. 9: 1838.
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