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Graph theoretical approaches to the ...

Yang, Qiaofeng.

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Graph theoretical approaches to the analysis of large-scale genomic data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Graph theoretical approaches to the analysis of large-scale genomic data./
作者:	Yang, Qiaofeng.
面頁冊數:	211 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-03, Section: B, page: 1536.
Contained By:	Dissertation Abstracts International67-03B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3210428
ISBN:	9780542599828

Graph theoretical approaches to the analysis of large-scale genomic data.
Yang, Qiaofeng.

Graph theoretical approaches to the analysis of large-scale genomic data. - 211 p.

Source: Dissertation Abstracts International, Volume: 67-03, Section: B, page: 1536.

Thesis (Ph.D.)--University of California, Riverside, 2006.

In this thesis, we focus on the design and application of graph theoretical approaches on a selected set of problems in computational biology.

ISBN: 9780542599828Subjects--Topical Terms:

626642
Computer Science.

Graph theoretical approaches to the analysis of large-scale genomic data.
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245 1 0 $a Graph theoretical approaches to the analysis of large-scale genomic data.
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500 $a Source: Dissertation Abstracts International, Volume: 67-03, Section: B, page: 1536.
500 $a Adviser: Stefano Lonardi.
502 $a Thesis (Ph.D.)--University of California, Riverside, 2006.
520 $a In this thesis, we focus on the design and application of graph theoretical approaches on a selected set of problems in computational biology.
520 $a The first problem addressed here is known as the biclustering problem. The problem arises for certain types of microarray experiments which are performed under heterogeneous conditions. In these cases one can expect that only a subset of genes show similar expression pattern across a subset of conditions. Therefore, one not only has to "cluster genes", but also has to "cluster conditions". We first prove that the biclustering problem is computationally hard by reduction to maximum edge biclique problem. Then, we propose a fast and efficient randomized algorithm by random projections. A detailed probabilistic analysis of the algorithm and an asymptotic study on the statistical significance of the solutions are also given.
520 $a The second problem addressed is the analysis and modeling of complex biological networks, in particular, protein-protein interaction (PPI) networks. The study consists of two parts. First, we propose a novel algorithm for the de novo identification of the network building modules of PPI networks. Our graph decomposition algorithm is based on the notion of edge betweenness, and is capable of discovering network modules without any a priori knowledge. Preliminary results show that our method is capable of distinguishing more accurately networks known to have distinct topologies. In the second part of the study, we perform a detailed comparative characterization of the topological organization of PPI networks. We observe that PPI networks can be considered analogous to communication networks because most of cellular activities are conducted through protein protein interactions. Therefore, man-made communication networks, such as AS-level Internet networks, can constitute a reference point when attempting to understand the design principles underlying PPI networks. Our comparative study is primarily based on a comprehensive set of graph metrics that are aimed at characterizing a wide range of topological properties of both types of networks. The preliminary results show that, despite of the fact that both PPI and AS-level networks are scale-free and small-world, they are significantly different, arguably due to distinct design principles and constraints they need to satisfy.
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