NDLI: Investigating the Minimum Required Number of Genes for the Classification of Neuromuscular Disease Microarray Data

Content Provider	IEEE Xplore Digital Library
Author	Sakellariou, A. Sanoudou, D. Spyrou, G.
Copyright Year	1997
Abstract	The discovery of potential microarray markers, which will expedite molecular diagnosis/prognosis and provide reliable results to clinical decision-making and treatment selection for patients, is of paramount importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most statistically significant genes, are a powerful approach toward this goal. In this paper, we investigate the minimum required subsets of genes, which best classify neuromuscular disease data. For this purpose, we implemented a methodology pipeline that facilitated the use of multiple feature selection methods and subsequent performance of data classification. Five feature selection methods on datasets from ten different neuromuscular diseases were utilized. Our findings reveal subsets of very small number of genes, which can successfully classify normal/disease samples. Interestingly, we observe that similar classification results may be obtained from different subsets of genes. The proposed methodology can expedite the identification of small gene subsets with high-classification accuracy that could ultimately be used in the genetics clinics for diagnostic, prognostic, and pharmacogenomic purposes.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Computer Society
Page Count	7
File Size	547998
Starting Page	349
Ending Page	355
File Format	PDF
ISSN	10897771
Volume Number	15
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Neuromuscular Accuracy Probes Pipelines Classification algorithms Biomarkers neuromuscular diseases Feature selection microarray data analysis molecular diagnosis
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Biotechnology

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