Mining Gene Regulation Networks Based on the Categories Divided by Gene Ontology Annotations
Date Issued
2007
Date
2007
Author(s)
Lin, Ming-Chih
DOI
en-US
Abstract
The gene regulation networks can help biologists know more about the systematical biological phenomena. In this thesis, we integrate time-series microarray and the Gene Ontology, and propose a data mining approach to find gene regulation networks between gene categories.
We first transform the time-series microarray dataset into gene tendency profiles and use the Gene Ontology annotations to classify genes into gene categories. For each gene category, we find its regulation patterns. By using the regulation patterns found for each gene category, we can infer the gene regulation relationships by finding the inclusive and opposite patterns between gene categories. Base on the regulation relationships inferred, we can construct gene regulation networks.
The experiment results show that our proposed method is efficient and scalable. Our method can provide a global view of gene regulation networks, which include not only some meaningful regulation relationships verified by biologists, but also some regulation relationships share regulation patterns, which need to be further verified by biologists.
Subjects
時間序列生物晶片
基因本體論
資料探勘
基因群
基因調節網路
time-series microarray
Gene Ontology
data mining
gene category
gene regulation network
Type
other
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