東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Computational Methods for Comparativ...

Frelinger, Jacob.

Linked to FindBook

Google Book

Amazon

博客來

Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry./
Author:	Frelinger, Jacob.
Description:	100 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
Contained By:	Dissertation Abstracts International75-02B(E).
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3601327
ISBN:	9781303522369

Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry.
Frelinger, Jacob.

Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry. - 100 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--Duke University, 2013.

Automated analysis techniques for flow cytometry data can address many of the limitations of manual analysis by providing an objective approach for the identification of cellular subsets. While automated analysis has the potential to significantly improve automated analysis, challenges remain for automated methods in cross sample analysis for large scale studies. This thesis presents new methods for data normalization, sample enrichment for rare events of interest, and cell subset relabeling. These methods build upon and extend the use of Gaussian mixture models in automated flow cytometry analysis to enable practical large scale cell subset identification.

ISBN: 9781303522369Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry.
LDR:01558nam a2200289 4500 001 1965124
005 20141010093005.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303522369
035 $a (MiAaPQ)AAI3601327
035 $a AAI3601327
040 $a MiAaPQ $c MiAaPQ
100 1 $a Frelinger, Jacob. $3 2101738
245 1 0 $a Computational Methods for Comparative Analysis of Rare Cell Subsets in Flow Cytometry.
300 $a 100 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
500 $a Adviser: Cliburn Chan.
502 $a Thesis (Ph.D.)--Duke University, 2013.
520 $a Automated analysis techniques for flow cytometry data can address many of the limitations of manual analysis by providing an objective approach for the identification of cellular subsets. While automated analysis has the potential to significantly improve automated analysis, challenges remain for automated methods in cross sample analysis for large scale studies. This thesis presents new methods for data normalization, sample enrichment for rare events of interest, and cell subset relabeling. These methods build upon and extend the use of Gaussian mixture models in automated flow cytometry analysis to enable practical large scale cell subset identification.
590 $a School code: 0066.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Health Sciences, Immunology. $3 1017716
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0982
690 $a 0984
710 2 $a Duke University. $b Computational Biology and Bioinformatics. $3 1279998
773 0 $t Dissertation Abstracts International $g 75-02B(E).
790 $a 0066
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3601327