Article Abstract

	Title:	Text analysis of MEDLINE for discovering functional relationships among genes: evaluation of keyword extraction weighting schemes
	Author:	Ying Liu, Shamkant B. Navathe, Alex Pivoshenko, Venu G. Dasigi, Ray Dingledine, Brian J. Ciliax
	Address:	Laboratory for Bioinformatics and Medical Informatics, Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75083-0688, USA. ' Georgia Institute of Technology, College of Computing, 801 Atlantic Drive, Atlanta, GA 30322, USA. ' Georgia Institute of Technology, College of Computing, 801 Atlantic Drive, Atlanta, GA 30322, USA. ' Department of Computer Science, School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA 30060, USA. ' Department of Pharmacology, Emory University School of Medicine, Atlanta, GA 30322, USA. ' Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
	Journal:	International Journal of Data Mining and Bioinformatics 2006 - Vol. 1, No.1  pp. 88 - 110
	Abstract:	One of the key challenges of microarray studies is to derive biological insights from the gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the functional links among genes. However, the quality of the keyword lists significantly affects the clustering results. We compared two keyword weighting schemes: normalised z-score and term frequency–inverse document frequency (TFIDF). Two gene sets were tested to evaluate the effectiveness of the weighting schemes for keyword extraction for gene clustering. Using established measures of cluster quality, the results produced from TFIDF-weighted keywords outperformed those produced from normalised z-score weighted keywords. The optimised algorithms should be useful for partitioning genes from microarray lists into functionally discrete clusters.
	Keywords:	DNA microarray lists; information extraction; MEDLINE; text analysis; BEA-PARTITION; clustering analysis; gene expression patterns; keyword extraction; keyword weighting; data mining; bioinformatics; keyword association; gene clustering.
	DOI:	10.1504/IJDMB.2006.009923
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