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Generalizations of the topological o...

University of California, Los Angeles.

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Generalizations of the topological overlap measure for neighborhood analysis and module detection in gene and protein networks.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Generalizations of the topological overlap measure for neighborhood analysis and module detection in gene and protein networks./
作者:	Li, Ai.
面頁冊數:	68 p.
附註:	Adviser: Steve Horvath.
Contained By:	Dissertation Abstracts International69-02B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3302568
ISBN:	9780549483656

Generalizations of the topological overlap measure for neighborhood analysis and module detection in gene and protein networks.
Li, Ai.

Generalizations of the topological overlap measure for neighborhood analysis and module detection in gene and protein networks. - 68 p.

Adviser: Steve Horvath.

Thesis (Ph.D.)--University of California, Los Angeles, 2008.

Availability. A executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the following webpage:

ISBN: 9780549483656Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Generalizations of the topological overlap measure for neighborhood analysis and module detection in gene and protein networks.
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100 1 $a Li, Ai. $3 1023484
245 1 0 $a Generalizations of the topological overlap measure for neighborhood analysis and module detection in gene and protein networks.
300 $a 68 p.
500 $a Adviser: Steve Horvath.
500 $a Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 0768.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2008.
520 $a Availability. A executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the following webpage:
520 $a http://www.genetics.ucla.edu/labs/horvath/MTOM/
520 $a Motivation. In network analysis the goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes while the goal of module detection is to find the sets of genes which are highly interacted with each other. These two analysis are important in systems biology. If individual network connections are susceptible to noise, it can he advantageous to define similarity between nodes on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. It can be advantageous to define multi-node similarity measures because such multiple nodes similarity provides a direct view of the relationship among a set of nodes instead of only pair-wise relationship.
520 $a Results. The pairwise topological overlap measure is generalized to multiple network nodes(MTOM). For neighborhood analysis MTOM is subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. For module detection we propose the Module Affinity Search Technique (MAST) which is a modification of Cluster Affinity Search Technique and feed it with MTOM. We provide empirical and simulation evidence that the resulting neighborhoods and modules are biologically meaningful, e.g. we use neighborhood analysis and module detection to brain cancer gene co-expression network.
590 $a School code: 0031.
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0308
710 2 $a University of California, Los Angeles. $3 626622
773 0 $t Dissertation Abstracts International $g 69-02B.
790 $a 0031
790 1 0 $a Horvath, Steve, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3302568