NDLI: Dealing with missing values in microarray data

Content Provider	IEEE Xplore Digital Library
Author	Mohammadi, A. Saraee, M.H.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Isfahan Univ. of Technol., Isfahan (Mohammadi, A.; Saraee, M.H.)
Abstract	Gene expression profiling plays an important role in a broad range of areas in biology. The raw gene expression data, may contain missing values. It is an important preprocessing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis. Numerous methods have been developed to deal with missing values. In this paper, a new and robust method based on fuzzy clustering and gene ontology is proposed to estimate missing values in microarray data. In the proposed method, missing values are imputed with values generated from cluster centers. To determine the similar genes in clustering process, we have utilized the biological knowledge obtained from gene ontology as well as gene expression values. We have applied the proposed method on yeast cell cycle data and yeast environmental stress data, with different percentage of missing entries. We compared the estimation accuracy of our method with some other methods. The experimental results indicate that the proposed method outperforms other methods in terms of accuracy.
Starting Page	258
Ending Page	263
File Size	3572106
Page Count	6
File Format	PDF
ISBN	9781424422104
DOI	10.1109/ICET.2008.4777511
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Pharmaceutical technology Ontologies Biology Gene expression Stress Diseases Fungi microarray Clustering algorithms gene ontoloy fuzzy clustering Robustness gene expression missing values
Content Type	Text
Resource Type	Article

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