Detecting gene relations from Medline abstracts.
Research in bioinformatics in the past decade has generated a large volume of textual biological data stored in databases such as MEDLINE. It takes a copious amount of effort and time, even for expert users, to manually extract useful information embedded in such a large volume of retrieved data and automated intelligent text analysis tools are increasingly becoming essential. In this article, we present a simple analysis and knowledge discovery method that can identify related genes as well as their shared functionality (if any) based on a collection of relevant retrieved relevant MEDLINE documents. The relative computational simplicity of the proposed method makes it possible to process and analyze large volumes of data in a short time. Hence, it significantly contributes to and enhances a user's ability to discover such embedded information. Two case studies are presented that indicate the usefulness of the proposed method.
Department of Computer & Information Science, Indiana University, Purdue University Indianapolis, Indianapolis, Indiana 46202, USA.
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M
Stephens
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M
Palakal
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S
Mukhopadhyay
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R
Raje
J
J
Mostafa
D000043:Abstracting and Indexing; D000465:Algorithms; D019295:Computational Biology; D016208:Databases, Factual; D005796:Genes; D016239:MEDLINE