東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Development of Map/Reduce based micr...

Yang, Guangyu.

FindBook

Google Book

Amazon

博客來

Development of Map/Reduce based microarray analysis tools.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Development of Map/Reduce based microarray analysis tools./
作者:	Yang, Guangyu.
面頁冊數:	72 p.
附註:	Source: Masters Abstracts International, Volume: 52-02.
Contained By:	Masters Abstracts International52-02(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1544222
ISBN:	9781303337536

Development of Map/Reduce based microarray analysis tools.
Yang, Guangyu.

Development of Map/Reduce based microarray analysis tools. - 72 p.

Source: Masters Abstracts International, Volume: 52-02.

Thesis (M.Engr.)--Clemson University, 2013.

High density oligonucleotide array (microarray) from the Affymetrix GeneChipRTM system has been widely used for the measurements of gene expressions. Currently, public data repositories, such as Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI), have accumulated very large amount of microarray data. For example, there are 84389 human and 9654 Arabidopsis microarray experiments in GEO database. Efficiently integrative analysis large amount of microarray data will provide more knowledge about the biological systems. Traditional microarray analysis tools all implemented sequential algorithms and can only be run on single processor. They are not able to handle very large microarray data sets with thousands of experiments. It is necessary to develop new microarray analysis tools using parallel framework. In this thesis, I implemented microarray quality assessment, background correction, normalization and summarization algorithms using the Map/Reduce framework. The Map/Reduce framework, first introduced by Google in 2004, offers a promising paradigm to develop scalable parallel applications for large-scale data. Evaluation of our new implementation on large microarray data of rice and Arabidopsis showed that they have good speedups. For example, running rice microarray data using our implementations of MAS5.0 algorithms on 20 computer nodes totally 320 processors has a 28 times speedup over using previous C++ implementation on single processor. Our new microarray tools will make it possible to utilize the valuable experiments in the public repositories.

ISBN: 9781303337536Subjects--Topical Terms:

626642
Computer Science.

Development of Map/Reduce based microarray analysis tools.
LDR:02433nam a2200277 4500 001 1965660
005 20141029122147.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303337536
035 $a (MiAaPQ)AAI1544222
035 $a AAI1544222
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yang, Guangyu. $3 2102349
245 1 0 $a Development of Map/Reduce based microarray analysis tools.
300 $a 72 p.
500 $a Source: Masters Abstracts International, Volume: 52-02.
500 $a Adviser: Feng Luo.
502 $a Thesis (M.Engr.)--Clemson University, 2013.
520 $a High density oligonucleotide array (microarray) from the Affymetrix GeneChipRTM system has been widely used for the measurements of gene expressions. Currently, public data repositories, such as Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI), have accumulated very large amount of microarray data. For example, there are 84389 human and 9654 Arabidopsis microarray experiments in GEO database. Efficiently integrative analysis large amount of microarray data will provide more knowledge about the biological systems. Traditional microarray analysis tools all implemented sequential algorithms and can only be run on single processor. They are not able to handle very large microarray data sets with thousands of experiments. It is necessary to develop new microarray analysis tools using parallel framework. In this thesis, I implemented microarray quality assessment, background correction, normalization and summarization algorithms using the Map/Reduce framework. The Map/Reduce framework, first introduced by Google in 2004, offers a promising paradigm to develop scalable parallel applications for large-scale data. Evaluation of our new implementation on large microarray data of rice and Arabidopsis showed that they have good speedups. For example, running rice microarray data using our implementations of MAS5.0 algorithms on 20 computer nodes totally 320 processors has a 28 times speedup over using previous C++ implementation on single processor. Our new microarray tools will make it possible to utilize the valuable experiments in the public repositories.
590 $a School code: 0050.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0984
690 $a 0715
710 2 $a Clemson University. $b Computer Science. $3 1030701
773 0 $t Masters Abstracts International $g 52-02(E).
790 $a 0050
791 $a M.Engr.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1544222