Identifying the Subfamilies of Voltage-Gated Potassium Channels Using Feature Selection Technique

Abstract

Voltage-gated K+ channel (VKC) plays important roles in biology procession, especially in nervous system. Different subfamilies of VKCs have different biological functions. Thus, knowing VKCs’ subfamilies has become a meaningful job because it can guide the direction for the disease diagnosis and drug design. However, the traditional wet-experimental methods were costly and time-consuming. It is highly desirable to develop an effective and powerful computational tool for identifying different subfamilies of VKCs. In this study, a predictor, called iVKC-OTC, has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of VKCs. One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions. It was observed on a benchmark dataset, by using a jackknife test, that the overall accuracy achieved by iVKC-OTC reaches to 96.77% in identifying the six subfamilies of VKCs, indicating that the new predictor is promising or at least may become a complementary tool to the existing methods in this area. It has not escaped our notice that the optimized tripeptide composition can also be used to investigate other protein classification problems.

Briefing in Application of Machine Learning Methods in Ion Channel Prediction

“…For the convenience of the vast majority of experimental scientists, a predictive tool, called VKCPred, was constructed and can be freely downloaded from http://cobi.uestc.edu.cn/people/hlin/tools/VKCPred/ . For further improving the accuracy, Liu et al [ 18 ] proposed BD-based feature selection technique to pick out optimal tripeptides. The LibSVM was used to execute the SVM algorithm.…”

“…For a stochastic event, two possible cases, namely, occurrence and nonoccurrence, will happen when one observes the i th feature occurring in the k th type set [ 18 ]. Each outcome has a fixed probability when benchmark dataset has been fixed.…”

Predicting cancerlectins by the optimal g-gap dipeptides

“…It has been proved that the optimized parameters could improve predictive accuracy 47 48 49 50 . Moreover, the high dimension vector in feature set would cause dimension disaster and will lead to a handicap for the computation or increase of computational time 38 .…”

LiGIoNs: A Computational Method for the Detection and Classification of Ligand-Gated Ion Channels

“…LGIC subfamily Subunit Count Considering the importance of ion channels for normal cellular function and their designated role as drug targets [18,19], several methods have been developed for the prediction of these proteins using information encoded exclusively in their amino acid sequence [20][21][22][23][24][25][26][27]. Ion channel prediction is mainly based on SVMs, and several feature selection techniques have been used to train the machine learning classifiers, mainly for the prediction and classification of VGICs.…”