Abstract

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.