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Text mining for molecular network-ba...

Krauthammer, Michael Olivier.

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Text mining for molecular network-based toxicity prediction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Text mining for molecular network-based toxicity prediction./
Author:	Krauthammer, Michael Olivier.
Description:	126 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4139.
Contained By:	Dissertation Abstracts International64-09B.
Subject:	Biology, General. -
Online resource:	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.
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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
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690 $a 0566
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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