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The iridescent system : an automated data-mining method to identify, evaluate, and analyze sets of relationships within textual databases

by Wren, Jonathan Daniel.

Abstract (Summary)
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. vii
Bibliographical Information:

Advisor:

School:The University of Texas Southwestern Medical Center at Dallas

School Location:USA - Texas

Source Type:Master's Thesis

Keywords:information storage and retrieval databases systems computing methodologies automatic data processing artificial intelligence neural networks computer dissertations academic texas

ISBN:

Date of Publication:01/01/2000

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