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» Microarray Gene Expression Data Analysis
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RECOMB
2002
Springer
15 years 10 months ago
A new approach to analyzing gene expression time series data
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved timepoints, clustering, and dataset alignment. Each expression p...
Ziv Bar-Joseph, Georg Gerber, David K. Gifford, To...
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
15 years 10 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
ISNN
2004
Springer
15 years 3 months ago
A Novel Clustering Analysis Based on PCA and SOMs for Gene Expression Patterns
This paper proposes a novel clustering analysis algorithm based on principal component analysis (PCA) and self-organizing maps (SOMs) for clustering the gene expression patterns. T...
Hong-Qiang Wang, De-Shuang Huang, Xing-Ming Zhao, ...
BMCBI
2006
140views more  BMCBI 2006»
14 years 9 months ago
Feature selection using Haar wavelet power spectrum
Background: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical m...
Prabakaran Subramani, Rajendra Sahu, Shekhar Verma
BMCBI
2010
97views more  BMCBI 2010»
14 years 7 months ago
Preprocessing of gene expression data by optimally robust estimators
Background: The preprocessing of gene expression data obtained from several platforms routinely includes the aggregation of multiple raw signal intensities to one expression value...
Matthias Kohl, Hans-Peter Deigner