The IRIDESCENT System: An Automated Data-Mining Method to Identify, Evaluate, and Analyze Sets of Relationships Within Textual Databases

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Title: The IRIDESCENT System: An Automated Data-Mining Method to Identify, Evaluate, and Analyze Sets of Relationships Within Textual Databases
Author: Wren, Jonathan Daniel
Abstract: Individuals are limited in their ability to read , remember and compare relationships within the vast amount of scientific literature available . This is not only because the amount of literature is increasing exponentially , but the number of things being researched within is as well . Adding to the scale of analysis are new technologies that increase the rate by which data is being gathered from scientific experiments . For most areas of research interest , the scale of analysis exceeds an individual's ability to be aware of all the relationships contained within . Thus , an informatics approach is necessary to identify large -scale trends , shared relationships and novel relationships that are not contained within the literature , but are the logical consequence of the relationships that are . A system has been designed to establish a network of relationships between "objects" of research interest (e .g . genes , chemical compounds , drugs , diseases and clinical phenotypes ) by extracting information from scientific text in an automated manner . This system , called IRIDESCENT (Implicit Relationship IDEntification by in -Silico Construction of an Entity -based Network from Text ) , enables the discovery of novel relationships by identifying and scoring objects sharing large sets of relationships with an object of interest . IRIDESCENT also allows sets of objects to be analyzed for shared relationships , such as responding genes from a microarray experiment . Herein is described the development and workings of IRIDESCENT as well as several well -developed applications of the system .
URI: http : / /hdl .handle .net /2152 .5 /230
Date: 2003-02-01

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The IRIDESCENT System: An Automated Data-Mining Method to Identify, Evaluate, and Analyze Sets of Relationships Within Textual Databases. Graduate School of Biomedical Sciences. Available electronically from http : / /hdl .handle .net /2152 .5 /230 .

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