title: Robust prediction of protein subcellular localization combining PCA and WSVMs.
abstract: Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy.
authors: China Software Testing Center, China Center for Information Industry Development, Beijing 100048, China. tianjiang@gmail.com
authors: Jiang
authors: J
authors: Tian
authors: Hong
authors: H
authors: Gu
authors: Wenqi
authors: W
authors: Liu
authors: Chiyang
authors: C
authors: Gao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005057:Eukaryotic Cells / Q000737:chemistry / Q000378:metabolism; D042541:Intracellular Space / Q000737:chemistry* / Q000378:metabolism; D025341:Principal Component Analysis / Q000379:methods*; D011387:Prokaryotic Cells / Q000737:chemistry / Q000378:metabolism; D021381:Protein Transport; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins