Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28585
Title: Incremental fuzzy mining of gene expression data for gene function prediction
Authors: Ma, PCH
Chan, KCC 
Keywords: Bioinformatics
fuzzy data mining
gene expression data analysis
gene function prediction
pattern discovery
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on biomedical engineering, 2011, v. 58, no. 5, 5447718, p. 1246-1252 How to cite?
Journal: IEEE transactions on biomedical engineering 
Abstract: Due to the complexity of the underlying biological processes, gene expression data obtained from DNA microarray technologies are typically noisy and have very high dimensionality and these make the mining of such data for gene function prediction very difficult. To tackle these difficulties, we propose to use an incremental fuzzy mining technique called incremental fuzzy mining (IFM). By transforming quantitative expression values into linguistic terms, such as highly or lowly expressed, IFM can effectively capture heterogeneity in expression data for pattern discovery. It does so using a fuzzy measure to determine if interesting association patterns exist between the linguistic gene expression levels. Based on these patterns, IFM can make accurate gene function predictions and these predictions can be made in such a way that each gene can be allowed to belong to more than one functional class with different degrees of membership. Gene function prediction problem can be formulated both as classification and clustering problems, and IFM can be used either as a classification technique or together with existing clustering algorithms to improve the cluster groupings discovered for greater prediction accuracies. IFM is characterized also by its being an incremental data mining technique so that the discovered patterns can be continually refined based only on newly collected data without the need for retraining using the whole dataset. For performance evaluation, IFM has been tested with real expression datasets for both classification and clustering tasks. Experimental results show that it can effectively uncover hidden patterns for accurate gene function predictions.
URI: http://hdl.handle.net/10397/28585
ISSN: 0018-9294
EISSN: 1558-2531
DOI: 10.1109/TBME.2010.2047724
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