東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Text mining the biomedical literatur...

Tanabe, Lorraine Kim.

Linked to FindBook

Google Book

Amazon

博客來

Text mining the biomedical literature for genetic knowledge.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Text mining the biomedical literature for genetic knowledge./
Author:	Tanabe, Lorraine Kim.
Description:	136 p.
Notes:	Director: Lawrence Hunter.
Contained By:	Dissertation Abstracts International64-02B.
Subject:	Biology, Genetics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3079362

Text mining the biomedical literature for genetic knowledge.
Tanabe, Lorraine Kim.

Text mining the biomedical literature for genetic knowledge. - 136 p.

Director: Lawrence Hunter.

Thesis (Ph.D.)--George Mason University, 2003.

Knowledge about genes and gene products continues to grow exponentially in electronic textual databases. Due to the complexity and homogeneity of the biomedical domain, synthesis of all of the available information about thousands of genes and proteins is a prohibitive task. Text mining can simplify this scenario by aiding researchers in finding relevant facts in vast biomedical textual databases. This thesis explores how natural language processing (NLP) and machine learning methods can be used to represent, filter, extract and infer genetic knowledge in biomedical text.Subjects--Topical Terms:

1017730
Biology, Genetics.

Text mining the biomedical literature for genetic knowledge.
LDR:01414nam 2200265 a 45 001 937541
005 20110511
008 110511s2003 eng d
035 $a (UnM)AAI3079362
035 $a AAI3079362
040 $a UnM $c UnM
100 1 $a Tanabe, Lorraine Kim. $3 1261402
245 1 0 $a Text mining the biomedical literature for genetic knowledge.
300 $a 136 p.
500 $a Director: Lawrence Hunter.
500 $a Source: Dissertation Abstracts International, Volume: 64-02, Section: B, page: 0817.
502 $a Thesis (Ph.D.)--George Mason University, 2003.
520 $a Knowledge about genes and gene products continues to grow exponentially in electronic textual databases. Due to the complexity and homogeneity of the biomedical domain, synthesis of all of the available information about thousands of genes and proteins is a prohibitive task. Text mining can simplify this scenario by aiding researchers in finding relevant facts in vast biomedical textual databases. This thesis explores how natural language processing (NLP) and machine learning methods can be used to represent, filter, extract and infer genetic knowledge in biomedical text.
590 $a School code: 0883.
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Computer Science. $3 626642
690 $a 0369
690 $a 0984
710 2 0 $a George Mason University. $3 1019450
773 0 $t Dissertation Abstracts International $g 64-02B.
790 $a 0883
790 1 0 $a Hunter, Lawrence, $e advisor
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3079362