An intelligent biological information management system.

Abstract

As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to sift through these vast volumes of heterogeneous data and analysis tools. In this paper we present a general model for an information management system that is adaptable and scalable, followed by a detailed design and implementation of one component of the model. The prototype, called BioSifter, was applied to problems in the bioinformatics area. BioSifter was tested using 500 documents obtained from PubMed database on two biological problems related to genetic polymorphism and extracorporal shockwave lithotripsy. The results indicate that BioSifter is a powerful tool for biological researchers to automatically retrieve relevant text documents from biological literature based on their interest profile. The results also indicate that the first stage of information management process, i.e. data to information transformation, significantly reduces the size of the information space. The filtered data obtained through BioSifter is relevant as well as much smaller in dimension compared to all the retrieved data. This would in turn significantly reduce the complexity associated with the next level transformation, i.e. information to knowledge.