Protein Complexes Classification Study Based on Machine Learning Approaches

• Main Authors: Kun-Ting Chao, 趙?廷
• Other Authors: chien-hung Huang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/8p6y29

碩士 === 國立虎尾科技大學 === 資訊工程研究所 === 99 === Abstract Protein complexes play important roles in many cellular processes. There are several approaches have been developed for protein complexes prediction; such as (1) using graph theory to study dense protein-protein interaction regions, (2) based on experimental data, such as tandem mass spectrometry, (3) the core attachment approach, and (4) heterogeneity data integration. All of these approaches have certain limitations for these approaches considering only the static, non-biochemical properties of a protein complex. In this thesis, we suggest to integrate various aspects of protein complexes property, i.e. staticas well as the physiochemical properties, and to describe protein complexes.Our method consists of three mainsteps; (i) estimation of parameter values and(ii) major parameters selection, and (iii) validationof classification accuracy.In the parameter estimation step, 27 parameters are considered. Principle component analysis (PCA) and logistic regression (LR) methods are used to determine the major features. In the validation step,major features are extracted from the previous step and are used to construct the feature vectors. After that, they are trained by two machine learning methods, i.e. support vector machines (SVM) and neural network (NN). The 6-fold cross-validation test is performed to investigate the classification accuracy of all major feature subsets. In case of combining Isoelectric point (pI) with GO annotation and sequence similarity features, the result indicates that it can achieve a slightly better classification accuracy. Taking the physiochemical properties for consideration, the present study could possibly improve the accuracy for protein complex prediction tools .