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Methods in biomedical text mining.

Columbia University.

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Methods in biomedical text mining.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Methods in biomedical text mining./
Author:	Rodriguez-Esteban, Raul.
Description:	133 p.
Notes:	Adviser: Andrey Rzhetsky.
Contained By:	Dissertation Abstracts International68-11B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3292142
ISBN:	9780549350316

Methods in biomedical text mining.
Rodriguez-Esteban, Raul.

Methods in biomedical text mining. - 133 p.

Adviser: Andrey Rzhetsky.

Thesis (Ph.D.)--Columbia University, 2007.

Methods to improve text raining of molecular biology interactions are needed to capture a richer information space and qualify the quality of extraction. Simple interaction models fail to describe contextual and confidence information that would help with more fine-grained analyses. Herein a method is presented to streamline curation of text-mined data mid a way to improve text mining of biomedical terms that can be adapted to other domains using different machine learning techniques. Those advances can be integrated into more powerful text-raining systems to meet user demand and to further promote the adoption of text-mining tools. Additionally, three studios oil the nature of biomedical publications are presented: their novelty hinges oil the fact, that each asks questions that had not been posed before. They cover the phenomena, of retraction, ways to improve the impact of research, and the writing style used in biomedical literature. Retraction is a hot topic in recent times but it has not been heeded in an analytical fashion. Measuring the impact of scientific publications has brought heated debate on which are bast at describing it. We propose a method not to measure impact, but to improve it. Finally, we analyze the influence of scientific writing style on the priming of its reader from a sensorial point of view.

ISBN: 9780549350316Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Methods in biomedical text mining.
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020 $a 9780549350316
035 $a (UMI)AAI3292142
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040 $a UMI $c UMI
100 1 $a Rodriguez-Esteban, Raul. $3 1019078
245 1 0 $a Methods in biomedical text mining.
300 $a 133 p.
500 $a Adviser: Andrey Rzhetsky.
500 $a Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7047.
502 $a Thesis (Ph.D.)--Columbia University, 2007.
520 $a Methods to improve text raining of molecular biology interactions are needed to capture a richer information space and qualify the quality of extraction. Simple interaction models fail to describe contextual and confidence information that would help with more fine-grained analyses. Herein a method is presented to streamline curation of text-mined data mid a way to improve text mining of biomedical terms that can be adapted to other domains using different machine learning techniques. Those advances can be integrated into more powerful text-raining systems to meet user demand and to further promote the adoption of text-mining tools. Additionally, three studios oil the nature of biomedical publications are presented: their novelty hinges oil the fact, that each asks questions that had not been posed before. They cover the phenomena, of retraction, ways to improve the impact of research, and the writing style used in biomedical literature. Retraction is a hot topic in recent times but it has not been heeded in an analytical fashion. Measuring the impact of scientific publications has brought heated debate on which are bast at describing it. We propose a method not to measure impact, but to improve it. Finally, we analyze the influence of scientific writing style on the priming of its reader from a sensorial point of view.
590 $a School code: 0054.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 68-11B.
790 $a 0054
790 1 0 $a Rzhetsky, Andrey, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3292142