國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Rich probabilistic models for genomi...

Segal, Eran.

Rich probabilistic models for genomic data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Rich probabilistic models for genomic data.
作者:	Segal, Eran.
面頁冊數:	272 p.
附註:	Adviser: Daphne Koller.
附註:	Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4679.
Contained By:	Dissertation Abstracts International65-09B.
標題:	Biology, Molecular.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145530
ISBN:	0496044737

Rich probabilistic models for genomic data.
Segal, Eran.

Rich probabilistic models for genomic data. - 272 p.

Adviser: Daphne Koller.

Thesis (Ph.D.)--Stanford University, 2004.

Genomic datasets, spanning many organisms and data types, are rapidly being produced, creating new opportunities for understanding the molecular mechanisms underlying human disease, and for studying complex biological processes on a global scale. Transforming these immense amounts of data into biological information is a challenging task. In this thesis, we address this challenge by presenting a statistical modeling language, based on Bayesian networks, for representing heterogeneous biological entities and modeling the mechanism by which they interact. We use statistical learning approaches in order to learn the details of these models (structure and parameters) automatically from raw genomic data. The biological insights are then derived directly from the learned model. We describe three applications of this framework to the study of gene regulation: (1) Understanding the process by which DNA patterns (motifs) in the control regions of genes play a role in controlling their activity. Using only DNA sequence and gene expression data as input, these models recovered many of the known motifs in yeast and several known motif combinations in human. (2) Finding regulatory modules and their actual regulator genes directly from gene expression data. Some of the predictions from this analysis were tested successfully in the wet-lab, suggesting regulatory roles for three previously uncharacterized proteins. (3) Combining gene expression profiles from several organisms for a more robust prediction of gene function and regulatory pathways, and for studying the degree to which regulatory relationships have been conserved across evolution.

ISBN: 0496044737Subjects--Topical Terms:

226919
Biology, Molecular.

Rich probabilistic models for genomic data.
LDR:02571nmm _2200265 _450 001 162879
005 20051017073532.5
008 090528s2004 eng d
020 $a 0496044737
035 $a 00149380
040 $a UnM $c UnM
100 0 $a Segal, Eran. $3 228024
245 1 0 $a Rich probabilistic models for genomic data.
300 $a 272 p.
500 $a Adviser: Daphne Koller.
500 $a Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4679.
502 $a Thesis (Ph.D.)--Stanford University, 2004.
520 # $a Genomic datasets, spanning many organisms and data types, are rapidly being produced, creating new opportunities for understanding the molecular mechanisms underlying human disease, and for studying complex biological processes on a global scale. Transforming these immense amounts of data into biological information is a challenging task. In this thesis, we address this challenge by presenting a statistical modeling language, based on Bayesian networks, for representing heterogeneous biological entities and modeling the mechanism by which they interact. We use statistical learning approaches in order to learn the details of these models (structure and parameters) automatically from raw genomic data. The biological insights are then derived directly from the learned model. We describe three applications of this framework to the study of gene regulation: (1) Understanding the process by which DNA patterns (motifs) in the control regions of genes play a role in controlling their activity. Using only DNA sequence and gene expression data as input, these models recovered many of the known motifs in yeast and several known motif combinations in human. (2) Finding regulatory modules and their actual regulator genes directly from gene expression data. Some of the predictions from this analysis were tested successfully in the wet-lab, suggesting regulatory roles for three previously uncharacterized proteins. (3) Combining gene expression profiles from several organisms for a more robust prediction of gene function and regulatory pathways, and for studying the degree to which regulatory relationships have been conserved across evolution.
590 $a School code: 0212.
650 # 0 $a Biology, Molecular. $3 226919
650 # 0 $a Computer Science. $3 212513
690 $a 0307
690 $a 0984
710 0 # $a Stanford University. $3 212607
773 0 # $g 65-09B. $t Dissertation Abstracts International
790 $a 0212
790 1 0 $a Koller, Daphne, $e advisor
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://libsw.nuk.edu.tw:81/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145530 $z http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145530