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Advanced data mining techniques for ...

Lu, Yi.

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Advanced data mining techniques for identifying correlation between gene expression and promoters.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced data mining techniques for identifying correlation between gene expression and promoters./
作者:	Lu, Yi.
面頁冊數:	119 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3236.
Contained By:	Dissertation Abstracts International67-06B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3225807
ISBN:	9780542764066

Advanced data mining techniques for identifying correlation between gene expression and promoters.
Lu, Yi.

Advanced data mining techniques for identifying correlation between gene expression and promoters. - 119 p.

Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3236.

Thesis (Ph.D.)--Wayne State University, 2006.

With the genetic code unveiled, one of the major challenges in the post-genomic era is to determine the regulatory networks of cells. An intermediate goal for revealing the transcription regulatory network of cells is to determine all DNA-binding transcription factors (TFs) and their regulatory binding sites within the genomes. The initial step to achieve this goal is to find the transcription factor binding sites within the regulatory regions of clustered co-expressed genes from microarray experiments, which includes a two-step solution: gene expression clustering followed by motif discovery. Although this approach id successful applied to small datasets, it is based on the assumption that co-expression implies co-regulation, which might always hold. Besides, genes are typically regulated by a combination of several TFs, which is not considered in most motif discovery algorithms. In this dissertation, we are mainly focus on designing a new gene expression clustering algorithm as well as a new algorithm to correlate the transcription binding sites with the gene expression.

ISBN: 9780542764066Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Advanced data mining techniques for identifying correlation between gene expression and promoters.
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520 $a With the genetic code unveiled, one of the major challenges in the post-genomic era is to determine the regulatory networks of cells. An intermediate goal for revealing the transcription regulatory network of cells is to determine all DNA-binding transcription factors (TFs) and their regulatory binding sites within the genomes. The initial step to achieve this goal is to find the transcription factor binding sites within the regulatory regions of clustered co-expressed genes from microarray experiments, which includes a two-step solution: gene expression clustering followed by motif discovery. Although this approach id successful applied to small datasets, it is based on the assumption that co-expression implies co-regulation, which might always hold. Besides, genes are typically regulated by a combination of several TFs, which is not considered in most motif discovery algorithms. In this dissertation, we are mainly focus on designing a new gene expression clustering algorithm as well as a new algorithm to correlate the transcription binding sites with the gene expression.
520 $a The most popular approaches in microarray gene clustering are K-means and hierarchical clustering. While the K-means clustering algorithm is initialization dependent and may converge to a local optimum, the hierarchical clustering algorithm needs human intervention because of its nested cluster structure. We have proposed a Fast Genetic K-means Algorithm (FGKA) with a guarantee of converging to the global optimum in theory. Our experiments show that the FGKA indeed converges to the global optimum and independent from initialization. FGKA and its variance, Incremental Genetic K-means Algorithm (IGKA) and Hybrid Genetic K-means Algorithm (HGKA) have been applied to cluster gene expression data of yeast and found that it increased the enrichment of genes of similar function within the cluster.
520 $a To understand the relationship between the motifs and gene expression changes, we proposed a new algorithm, Co-Miner (Correlation M&barbelow;iner). By using association rule mining technique, correlation rules were generated based on the expression profiles of genes with significant expression change through the time course of gene expression. In this way, we may consider the change in gene expression to be causatively associated with the transcription binding sites in the upstream sequences. By applying Co-Miner algorithm on yeast data set, the relationships between some motifs and gene expressions are confirmed in the literature.
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