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RECOMB
2002
Springer
14 years 8 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...
IJKDB
2010
170views more  IJKDB 2010»
13 years 5 months ago
Clustering Genes Using Heterogeneous Data Sources
Clustering of gene expression data is a standard exploratory technique used to identify closely related genes. Many other sources of data are also likely to be of great assistance...
Erliang Zeng, Chengyong Yang, Tao Li, Giri Narasim...
APBC
2004
132views Bioinformatics» more  APBC 2004»
13 years 9 months ago
A Novel Feature Selection Method to Improve Classification of Gene Expression Data
This paper introduces a novel method for minimum number of gene (feature) selection for a classification problem based on gene expression data with an objective function to maximi...
Liang Goh, Qun Song, Nikola K. Kasabov
BMCBI
2007
114views more  BMCBI 2007»
13 years 8 months ago
Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks
Background: Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As ...
Xiaohua Hu, Fang-Xiang Wu
PRL
2006
130views more  PRL 2006»
13 years 8 months ago
Efficient huge-scale feature selection with speciated genetic algorithm
With increasing interest in bioinformatics, sophisticated tools are required to efficiently analyze gene information. The classification of gene expression profiles is crucial in ...
Jin-Hyuk Hong, Sung-Bae Cho