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» Evaluation of clustering algorithms for gene expression data
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BIODATAMINING
2008
178views more  BIODATAMINING 2008»
15 years 1 months ago
Clustering-based approaches to SAGE data mining
Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical appli...
Haiying Wang, Huiru Zheng, Francisco Azuaje
WILF
2005
Springer
112views Fuzzy Logic» more  WILF 2005»
15 years 7 months ago
NEC for Gene Expression Analysis
Aim of this work is to apply a novel comprehensive machine learning tool for data mining to preprocessing and interpretation of gene expression data. Furthermore, some visualizatio...
Roberto Amato, Angelo Ciaramella, N. Deniskina, Ca...
BMCBI
2007
182views more  BMCBI 2007»
15 years 1 months ago
EDISA: extracting biclusters from multiple time-series of gene expression profiles
Background: Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consistin...
Jochen Supper, Martin Strauch, Dierk Wanke, Klaus ...
BMCBI
2004
120views more  BMCBI 2004»
15 years 1 months ago
Optimal cDNA microarray design using expressed sequence tags for organisms with limited genomic information
Background: Expression microarrays are increasingly used to characterize environmental responses and hostparasite interactions for many different organisms. Probe selection for cD...
Yian A. Chen, David J. Mckillen, Shuyuan Wu, Matth...
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BMCBI
2008
115views more  BMCBI 2008»
15 years 1 months ago
Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data
Background: Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differ...
Sudhakar Jonnalagadda, Rajagopalan Srinivasan