Building a Protein-Protein Interaction Prediction System based on Machine Learning Methods

• Main Authors: Fei-Hung Hung, 洪蜚鴻
• Other Authors: Hung-Wen Chiu
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/99248885276063812144

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 95 === INTRODUCTION Protein-protein interaction (PPI) is an emerging field in biological research and plays an important role in life process. If PPI prediction can be achieved, scientists will know biological processes and disease mechanisms better. Recently many PPI-related databases were produced. Besides, computational methods were applied to predict PPIs. Because functional regions, e.g. domains, motifs, are key components on whether one protein interact with another protein, several researches had attempted to use data mining methods to show the relationship of functional regions of proteins in PPIs without validation. MATERIALS AND METHODS In this study, PPI data were collected from DIP, IntAct and BIND, and the information of functional regions was downloaded from UNIPROT. These data were integrated into one database and its query interface was designed to present protein-protein interaction data including functional regions and sequences. This module for PPIs prediction based on an association rules mining was developed with three sets of PPI data and the PPIs in other species are used to evaluate our PPI prediction module. These rules were compared with the result of InterDom. Finally a system for PPI prediction was constructed with the module. RESULT A PPI prediction module was produced for a web-based system. The system will support queries for integrated protein information, protein-protein interaction information, the comparison between functional regions and sequences of proteins. Besides, the system can show those rules matched PPIs and those PPIs matched rules and give a PPI prediction function. Other related researches will be able to get integrated protein-protein interaction information and compare the functional regions by our system. The results of prediction will provide new references.