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» E-CAST: A Data Mining Algorithm for Gene Expression Data
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BIBE
2004
IEEE
107views Bioinformatics» more  BIBE 2004»
14 years 22 days ago
Enhanced pClustering and Its Applications to Gene Expression Data
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes an...
Sungroh Yoon, Christine Nardini, Luca Benini, Giov...
RECOMB
2002
Springer
14 years 9 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...
BMCBI
2007
179views more  BMCBI 2007»
13 years 9 months ago
Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data
Background: The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset anno...
Hui Yu, Feng Wang, Kang Tu, Lu Xie, Yuan-Yuan Li, ...
JCB
2007
130views more  JCB 2007»
13 years 8 months ago
Bayesian Inference of MicroRNA Targets from Sequence and Expression Data
MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although mu...
Jim C. Huang, Quaid Morris, Brendan J. Frey
CBMS
2005
IEEE
14 years 2 months ago
An Ontology-Driven Clustering Method for Supporting Gene Expression Analysis
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into ...
Haiying Wang, Francisco Azuaje, Olivier Bodenreide...