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

Text mining for molecular network-ba...

Krauthammer, Michael Olivier.

FindBook

Google Book

Amazon

博客來

Text mining for molecular network-based toxicity prediction.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Text mining for molecular network-based toxicity prediction./
作者:	Krauthammer, Michael Olivier.
面頁冊數:	126 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4139.
Contained By:	Dissertation Abstracts International64-09B.
標題:	Biology, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3104821

Text mining for molecular network-based toxicity prediction.
Krauthammer, Michael Olivier.

Text mining for molecular network-based toxicity prediction. - 126 p.

Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4139.

Thesis (Ph.D.)--Columbia University, 2003.

In a time of tremendous biomedical research activity linked to both the sequencing of the human genome and the availability of high-throughput technologies that measure functional aspects of the cell, it is important to foster the conception and implementation of methods that integrate the avalanche of new research data. In this thesis, we explore the use of text mining for automatically capturing molecular information from a rapidly expanding pool of scientific articles. We discuss methods of building intelligent tools to extracte biological facts from the literature, dates management issues related to large-scale text mining and the use of text mining to answer real-world biological questions. By using a literature-compiled molecular interaction network for the prediction of toxic drug affects, we demonstrate that the automated collection and integration of published, readily available biological information is a powerful method for tasting biomedical hypotheses.Subjects--Topical Terms:

1018625
Biology, General.

Text mining for molecular network-based toxicity prediction.
LDR:01840nmm 2200277 4500 001 1861387
005 20041111121754.5
008 130614s2003 eng d
035 $a (UnM)AAI3104821
035 $a AAI3104821
040 $a UnM $c UnM
100 1 $a Krauthammer, Michael Olivier. $3 1948988
245 1 0 $a Text mining for molecular network-based toxicity prediction.
300 $a 126 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4139.
500 $a Adviser: Andrey Rzhetsky.
502 $a Thesis (Ph.D.)--Columbia University, 2003.
520 $a In a time of tremendous biomedical research activity linked to both the sequencing of the human genome and the availability of high-throughput technologies that measure functional aspects of the cell, it is important to foster the conception and implementation of methods that integrate the avalanche of new research data. In this thesis, we explore the use of text mining for automatically capturing molecular information from a rapidly expanding pool of scientific articles. We discuss methods of building intelligent tools to extracte biological facts from the literature, dates management issues related to large-scale text mining and the use of text mining to answer real-world biological questions. By using a literature-compiled molecular interaction network for the prediction of toxic drug affects, we demonstrate that the automated collection and integration of published, readily available biological information is a powerful method for tasting biomedical hypotheses.
590 $a School code: 0054.
650 4 $a Biology, General. $3 1018625
650 4 $a Health Sciences, General. $3 1017817
650 4 $a Computer Science. $3 626642
690 $a 0306
690 $a 0566
690 $a 0984
710 2 0 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 64-09B.
790 1 0 $a Rzhetsky, Andrey, $e advisor
790 $a 0054
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3104821