Identifying Significant Transcriptional Modules by Genetic Algorithms from Gene Expression Data
Date Issued
2004
Date
2004
Author(s)
Chiu, Hua-Sheng
DOI
en-US
Abstract
Abstract
Transcriptional module is a set of genes that are co-regulated under particular experimental conditions. Genes in the same transcriptional modules are supposed to have the same function and regulated by the common transcriptional factors that bind to some promoter sequence in the upstream region. Identifying significant transcrip-tional modules may help biologists to reconstruct the whole genetic network between lots of organisms, and understanding the complex biological mechanisms in detailed. At present, utilizing microarray techniques, a high-throughput method for measuring gene expression, is an effective and intuitive way to achieve this goal. However, in the analysis of large-scale gene expression data from microarray, it's still a complicated topic until now for detecting transcriptional modules, even if several (bi-)clustering approaches are proposed.
In this thesis, the significant transcriptional modules are formulated to suit a novel model at first, and the goodness of a transcriptional module is clarified by the new definitions. Afterwards, a new biclustering approach is devised to treat identify-ing significant transcriptional modules among gene expression data as an optimization problem, and applying genetic algorithms to solve it for avoiding trapping into local optimal like other heuristic approaches. The special case of the proposed model is evaluating first for proving the effectiveness and correctness of the fitness function. At last, two large-scale gene expression data from Homo sapiens and Saccharomyces cerevisiae are both tested, and the derived significant transcriptional modules are evaluated again in silicon. These experimental results show that the proposed ap-proach is excellent in identifying significant transcriptional modules, and is also supe-rior to heuristics for detecting gene groups with more similar functional annotations.
Owing to the outstanding results, it's believed that the proposed approach is worthy of putting into advanced biological problems, such as phenotype classification in cancer research, functional predictions, and genetic network reconstruction et al.
Subjects
叢集
微陣列
遺傳演算法
轉錄模組
基因表現資料
transcriptional module
Genetic Algorithms
microarray
clustering
gene expression data
SDGs
Type
thesis
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