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

Pattern discovery in biological data...

Angelov, Stanislav Plamenov.

FindBook

Google Book

Amazon

博客來

Pattern discovery in biological data sets.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Pattern discovery in biological data sets./
作者:	Angelov, Stanislav Plamenov.
面頁冊數:	236 p.
附註:	Adviser: Sanjeev Khanna.
Contained By:	Dissertation Abstracts International68-04B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3260873

Pattern discovery in biological data sets.
Angelov, Stanislav Plamenov.

Pattern discovery in biological data sets. - 236 p.

Adviser: Sanjeev Khanna.

Thesis (Ph.D.)--University of Pennsylvania, 2007.

In recent years, we have seen a rapid increase in the available DNA and protein data coming from various genome sequencing projects. Such data is carefully studied for features reused by nature in order to understand the mechanisms of life. Many of these features are expressed as sequence patterns. Therefore, efficient computational methods to discover biologically significant motifs are highly desirable as they provide researchers with new insights into biological processes, causes of diseases, and evolution of life.Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Pattern discovery in biological data sets.
LDR:02539nam 2200289 a 45 001 947578
005 20110524
008 110524s2007 ||||||||||||||||| ||eng d
035 $a (UMI)AAI3260873
035 $a AAI3260873
040 $a UMI $c UMI
100 1 $a Angelov, Stanislav Plamenov. $3 1271048
245 1 0 $a Pattern discovery in biological data sets.
300 $a 236 p.
500 $a Adviser: Sanjeev Khanna.
500 $a Source: Dissertation Abstracts International, Volume: 68-04, Section: B, page: 2232.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2007.
520 $a In recent years, we have seen a rapid increase in the available DNA and protein data coming from various genome sequencing projects. Such data is carefully studied for features reused by nature in order to understand the mechanisms of life. Many of these features are expressed as sequence patterns. Therefore, efficient computational methods to discover biologically significant motifs are highly desirable as they provide researchers with new insights into biological processes, causes of diseases, and evolution of life.
520 $a There are two main approaches for extracting knowledge from sequence data. One approach compares newly acquired data with possibly, already annotated data under the assumption that data similarity implies functional similarity. The second approach mines the data for frequently occurring or surprising patterns. Such patterns are unlikely to occur at random and pinpoint candidates for further laboratory investigations.
520 $a In this thesis, we follow the above approaches to extract useful information from biological data sets such as DNA and protein sequences, as well as microarray-based gene expression profiles. Our contributions include linear time and near-linear time algorithms to enumerate short DNA substrings that contain evolutionary history, efficient algorithms for design of composite patterns with application to PCR, and new techniques for automated protein domain discovery using correlation clustering. We also give fast exact and approximation methods for nonparametric analysis of gene expression data using isotonic regression. In addition to these theoretical results, we implement our methods and analyze the findings on real, biological data.
590 $a School code: 0175.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Health Sciences, Immunology. $3 1017716
690 $a 0715
690 $a 0982
710 2 $a University of Pennsylvania. $3 1017401
773 0 $t Dissertation Abstracts International $g 68-04B.
790 $a 0175
790 1 0 $a Khanna, Sanjeev, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3260873