PCA and KPCA for Predicting Membrane Protein Types

Knowing type of an uncharacterized membrane protein often provides a useful clue in both basic research and drug discovery. With the explosion of protein sequences generated in the post genomic era, determination of membrane proteins types by experimental methods is expensive and time consuming. It therefore becomes important to develop an automated method to find the possible type of membrane protein. In view of this, the DC (Dipeptide Composition) is introduced to represent the protein sample. However, a high dimensional disaster may be caused by using this representation method. Thus, a linear dimensionality reduction algorithm PCA (Principle component analysis) and a nonlinear dimensionality reduction algorithm KPCA (Kernel Principle component analysis) are introduced to extract the indispensable features from the high-dimensional DC space,respectively. Based on the reduced low-dimensional features,K-NN (K-nearest neighbor) classifier is introduced to identify the types of membrane proteins. Finally, experiment results show that using the proposed method to cope with prediction of membrane proteins types are very effective.