NDLI: Membership scoring via independent feature subspace analysis for grouping co-expressed genes

Content Provider	IEEE Xplore Digital Library
Author	Heyjin Kim Seungjin Choi Sung-Yang Bang
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci. & Eng., POSTECH, Pohang, South Korea (Heyjin Kim; Seungjin Choi; Sung-Yang Bang)
Abstract	Linear decomposition models such as principal component analysis (PCA) and independent component analysis (ICA) were shown to be useful in analyzing high dimensional DNA microarray data, compared to clustering methods. Assuming that gene expression is controlled by a linear combination of uncorrelated/independent latent variables, linear modes were shown to be related to some biological functions. However, grouping co-expressed genes using these methods is not quite successful since they take some biological dependence into account. In this paper, we employ the independent feature subspace analysis (IFSA) method which finds phase- and shift-invariant features. We propose a new membership scoring method based on invariant features from IFSA and show its usefulness in grouping functionally-related genes in the presence of time-shift and expression phase variance. This is confirmed through PathCalling.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	1690
Ending Page	1695
File Size	406049
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223661
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Independent component analysis Principal component analysis Gene expression DNA Pattern matching Clustering methods Data analysis Brain modeling Computer science
Content Type	Text
Resource Type	Article

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