Mining Time Series Gene Expression Data for Gene regulatory Networks

• Main Authors: Yen-Fu Chen, 陳彥甫
• Other Authors: 李瑞庭
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/07054157665678117175

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 93 === Time series gene expression data can be exploited to reveal causal genetic events. However, current methods of gene network modeling focus on one sample of the dataset, which may suffer from a low recovery rate. Moreover, gene network modeling emphasizes small set of genes because of high computation time. Our proposed approach efficiently mines gene regulatory patterns from large scale of replicate time series datasets. The patterns can be used to generate gene regulatory networks. The regulatory networks reveal the relationships of dynamic causal regulatory events and their regulatory intensities. We first examine our proposed approach with simulated data for performance evaluation. In addition, we also apply our proposed approach to human cell cycle data. The results show that our proposed method is not only efficient and scalable but reveals complex regulatory information among large scale of genes.