Automated Gene-Retrieval System for Biological Information Needs

Imad Rahal (), Baoying Wang () and Riad Rahhal ()
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Imad Rahal: Computer Science Department, College of St. Benedict & St. John's University, Collegeville, MN 56321, USA
Baoying Wang: Department of Math/Computer Science, Waynesburg University, Waynesburg, PA 15370, USA
Riad Rahhal: Pediatrics Department, University of Iowa, Iowa City, IA 52242, USA

Journal of Information & Knowledge Management (JIKM), 2009, vol. 08, issue 01, 13-23

Abstract: In this day and age, conducting a biological experiment is presumably a very expensive procedure largely owing to the highly sophisticated and expensive equipment necessitated by the process. Conceivably, being capable of isolating and focusing on a smaller set of imperative genes or gene products that are of high relevance to the experiment, pathway, or biological system under investigation is very desirable largely owing to the potential savings in experimental costs. In this work, we propose an intelligent information system capable of generating a ranked list of genes and gene products that are most pertinent to a given biological pathway, experiment or system (referred to as a biological context henceforth). We assume that the biological context of interest can be described by various textual query terms and phrases from the biological domain which, in turn, relate to various molecular functions, biological processes and cellular components of genes and their products. Intelligent text-based analyses and mining are utilised for this purpose by using the published literature, in the form of publication abstracts downloaded fromPubMed, with the intention of ranking genes and gene products having identified relationships to the specified description terms based on the gene ontology (GO) standard. At this stage, our approach is capable of producing promising results given all surrounding restrictions, one of which is the lack of similar work in the literature. For demonstration purposes, we report experimental results on the molting regulation pathway inDrosophila melanogaster(fruit fly).

Keywords: Bioinformatics; gene ontology; text mining; gene ranking; querying non-conventional data sources; information retrieval (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1142/S0219649209002191

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