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Decoding heterogeneous big data in a...

Zhang, Xia.

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Decoding heterogeneous big data in an integrative way.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Decoding heterogeneous big data in an integrative way./
Author:	Zhang, Xia.
Description:	163 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
Contained By:	Dissertation Abstracts International75-05B(E).
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3610730
ISBN:	9781303706813

Decoding heterogeneous big data in an integrative way.
Zhang, Xia.

Decoding heterogeneous big data in an integrative way. - 163 p.

Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.

Thesis (Ph.D.)--Iowa State University, 2013.

This item is not available from ProQuest Dissertations & Theses.

Biotechnologies in post-genomic era, especially those that generate data in high-throughput, bring opportunities and challenges that are never faced before. And one of them is how to decode big heterogeneous data for clues that are useful for biological questions. With the exponential growth of a variety of data, comes with more and more applications of systematic approaches that investigate biological questions in an integrative way. Systematic approaches inherently require integration of heterogeneous information, which is urgently calling for a lot more efforts.

ISBN: 9781303706813Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Decoding heterogeneous big data in an integrative way.
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100 1 $a Zhang, Xia. $3 1914930
245 1 0 $a Decoding heterogeneous big data in an integrative way.
300 $a 163 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
500 $a Adviser: Jeanne M. Serb.
502 $a Thesis (Ph.D.)--Iowa State University, 2013.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Biotechnologies in post-genomic era, especially those that generate data in high-throughput, bring opportunities and challenges that are never faced before. And one of them is how to decode big heterogeneous data for clues that are useful for biological questions. With the exponential growth of a variety of data, comes with more and more applications of systematic approaches that investigate biological questions in an integrative way. Systematic approaches inherently require integration of heterogeneous information, which is urgently calling for a lot more efforts.
520 $a In this thesis, the effort is mainly devoted to the development of methods and tools that help to integrate big heterogeneous information. In Chapter 2, we employed a heuristic strategy to summarize/integrate genes that are essential for the determination of mouse retinal cells in the format of network. These networks with experimental evidence could be rediscovered in the analysis of high-throughput data set and thus would be useful in the leverage of high-throughput data. In Chapter 3, we described EnRICH, a tool that we developed to help qualitatively integrate heterogeneous intro-organism information. We also introduced how EnRICH could be applied to the construction of a composite network from different sources, and demonstrated how we used EnRICH to successfully prioritize retinal disease genes. Following the work of Chapter 3 (intro-organism information integration), in Chapter 4 we stepped to the development of method and tool that can help deal with inter-organism information integration. The method we proposed is able to match genes in a one-to-one fashion between any two genomes.
520 $a In summary, this thesis contributes to integrative analysis of big heterogeneous data by its work on the integration of intro- and inter-organism information.
590 $a School code: 0097.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Genetics. $3 1017730
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710 2 $a Iowa State University. $b Ecology, Evolution, and Organismal Biology. $3 1020986
773 0 $t Dissertation Abstracts International $g 75-05B(E).
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792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3610730