A method for improving protein localization prediction from datasets with outliers

Large-scale genome analysis and drug discovery require an automated prediction method for protein subcellular localization, and Support Vector Machines (SVMs) effectively solve this problem in a supervised manner. However, the protein subcellular localization datasets obtained from experiments always contain outliers, which can lead to poor generalization ability and classification accuracy. To address this issue, we first analyzed the influence of Principal Component Analysis (PCA) on classification performance, and then proposed a hybrid method for prediction of protein subcellular localization based on Weighted Supported Vector Machine (WSVM) and PCA. Different weights were assigned to different data points, so the training algorithm could learn the decision boundary according to the relative importance of the data points. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means (KPCM) was chosen to generate weights for this algorithm, as it generates relative high values for important data points but low values for outliers. Experimental results on a benchmark dataset show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy.