Mining Regulation Relationships between Gene Clusters by Using Time-Series Gene Expression Data
Date Issued
2006
Date
2006
Author(s)
Chen, Chieh-Chun
DOI
en-US
Abstract
Analyzing time series gene expression data provides a great opportunity to discover regulation relationships among genes and gene clusters. However, existing methods of mining gene regulation relationships, such as event method and q-cluster method, have their own limitations. The event method can only identify the relationships between gene pairs without the detailed time-lagged information and the q-cluster method limited by its pattern length can only find localized patterns. Therefore, in this thesis, we propose an approach that can efficiently mine all frequent regulation patterns without the limitation of pattern length and discover the regulation relationships among gene clusters with the detailed time-lagged information.
We first transform the raw data into a tendency matrix. Next, we group together genes sharing the same expression tendency over certain consecutive time points, and obtain their patterns and detailed information. Then, we extend the patterns obtained into longer patterns by a level-wise combination approach. Finally, we can analyze the characteristics of gene clusters and infer the regulation relationships among them. The experimental result demonstrates that our proposed method is efficient and scalable. Moreover, we use Gene Ontology and 439 regulation relationships proved by biologists to evaluate the effectiveness of our proposed method. The experimental result shows that our proposed method can reliably find those regulation relationships among gene clusters.
Subjects
時間性基因表現資料分析
基因群集
基因群集調節關係
資料探勘
Time-series gene expression data
gene clustering
gene regulation relationships
data mining
Type
other
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