An Effective Approach for Mining Time-Series Gene Expression Profile

Abstract

Time-series data analysis is an important problem in data mining fields due to the wide applications. Although some time-series analysis methods have been developed in recent years, they can not effectively resolve the fundamental problems in time-series gene expression mining in terms of scale transformation, offset transformation, time delay and noises. In this paper, we propose an effective approach for mining time-series data and apply it on time-series gene expression profile analysis. The proposed method utilizes dynamic programming technique and correlation coefficient measure to find the best alignment between the time-series expressions under the allowed number of noises. Through experimental evaluation, our method was shown to effectively resolve the four problems described above simultaneously. Hence, it can find the correct similarity and imply biological relationships between gene expressions.