語系:
繁體中文
English
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
Biological knowledge discovery throu...
~
Chen, Yu.
FindBook
Google Book
Amazon
博客來
Biological knowledge discovery through mining multiple sources of high-throughput data.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Biological knowledge discovery through mining multiple sources of high-throughput data./
作者:
Chen, Yu.
面頁冊數:
162 p.
附註:
Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3298.
Contained By:
Dissertation Abstracts International65-07B.
標題:
Biology, Molecular. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3141802
ISBN:
049688882X
Biological knowledge discovery through mining multiple sources of high-throughput data.
Chen, Yu.
Biological knowledge discovery through mining multiple sources of high-throughput data.
- 162 p.
Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3298.
Thesis (Ph.D.)--The University of Tennessee, 2004.
As we are moving into the post-genomic era, various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale. The high-throughput data are becoming fundamentally important resources to shed new insights on system-level understanding of the ' organization' and 'dynamics' of molecules (i.e. genes, proteins), relationships between them, interaction cascades, pathways, modules and various networks (i.e. regulation, co-expression and metabolism). This dissertation focuses on developing computational tools to facilitate the process of translating the ever-growing volumes of high-throughput data into significant biological knowledge on protein functions, pathways and modules.
ISBN: 049688882XSubjects--Topical Terms:
1017719
Biology, Molecular.
Biological knowledge discovery through mining multiple sources of high-throughput data.
LDR
:03481nmm 2200325 4500
001
1848994
005
20051202085220.5
008
130614s2004 eng d
020
$a
049688882X
035
$a
(UnM)AAI3141802
035
$a
AAI3141802
040
$a
UnM
$c
UnM
100
1
$a
Chen, Yu.
$3
1260328
245
1 0
$a
Biological knowledge discovery through mining multiple sources of high-throughput data.
300
$a
162 p.
500
$a
Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3298.
500
$a
Major Professor: Dong Xu.
502
$a
Thesis (Ph.D.)--The University of Tennessee, 2004.
520
$a
As we are moving into the post-genomic era, various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale. The high-throughput data are becoming fundamentally important resources to shed new insights on system-level understanding of the ' organization' and 'dynamics' of molecules (i.e. genes, proteins), relationships between them, interaction cascades, pathways, modules and various networks (i.e. regulation, co-expression and metabolism). This dissertation focuses on developing computational tools to facilitate the process of translating the ever-growing volumes of high-throughput data into significant biological knowledge on protein functions, pathways and modules.
520
$a
Although high-throughput data provide a global picture of biological systems about the underlying mechanisms, the details are often noisy, hence the integration of heterogeneous data that characterize cellular systems from different aspects (i.e. gene expression and protein-protein interactions) can lead to the comprehensive and coherent discoveries of biological insights. We developed a Bayesian probability framework to predict function for unannotated proteins in yeast through integrating protein binary interaction data, protein complex data and microarray data. We also extended the computational framework to infer biological pathway in an automated and systematical fashion.
520
$a
Besides bottom-up approaches moving from protein functions to pathways, we also applied top-down approaches to model the cellular network, that is, we started from the architecture of cellular network to identify the functional modules. We applied the k-core algorithm to decompose protein interaction and microarray gene co-expression networks, which provides strong support for modularity principles of networks' structure and function. Dynamic functional modules and protein complexes have been identified by clustering the network constructed from multiple sources of high-throughput data, shedding insights into understanding the organization and dynamics of a living cell.
520
$a
We also proposed a consensus approach to model biological pathway by combining different computational tools and integrating multiple sources of high-throughput data. In the future, with the explosion in the quantity and diversity of high-throughput data, it is vital to develop methodologies and innovative tools in bioinformatics to model biological systems and explore biological knowledge in an iterative fashion.
590
$a
School code: 0226.
650
4
$a
Biology, Molecular.
$3
1017719
650
4
$a
Biology, Biostatistics.
$3
1018416
650
4
$a
Computer Science.
$3
626642
690
$a
0307
690
$a
0308
690
$a
0984
710
2 0
$a
The University of Tennessee.
$3
1022026
773
0
$t
Dissertation Abstracts International
$g
65-07B.
790
1 0
$a
Xu, Dong,
$e
advisor
790
$a
0226
791
$a
Ph.D.
792
$a
2004
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3141802
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9198508
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入