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» Clustering cancer gene expression data: a comparative study
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ISBRA
2007
Springer
16 years 13 days ago
Noise-Based Feature Perturbation as a Selection Method for Microarray Data
Abstract. DNA microarrays can monitor the expression levels of thousands of genes simultaneously, providing the opportunity for the identification of genes that are differentiall...
Li Chen, Dmitry B. Goldgof, Lawrence O. Hall, Stev...
BMCBI
2006
92views more  BMCBI 2006»
15 years 6 months ago
Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays
Background: Genetic markers hold great promise for refining our ability to establish precise prognostic prediction for diseases. The development of comprehensive gene expression m...
Shigeyuki Matsui
BMCBI
2006
203views more  BMCBI 2006»
15 years 6 months ago
Independent component analysis reveals new and biologically significant structures in micro array data
Background: An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem...
Attila Frigyesi, Srinivas Veerla, David Lindgren, ...
ISBRA
2007
Springer
16 years 13 days ago
Discovering Relations Among GO-Annotated Clusters by Graph Kernel Methods
The biological interpretation of large-scale gene expression data is one of the challenges in current bioinformatics. The state-of-theart approach is to perform clustering and then...
Italo Zoppis, Daniele Merico, Marco Antoniotti, Bu...
JCP
2006
157views more  JCP 2006»
15 years 6 months ago
CF-GeNe: Fuzzy Framework for Robust Gene Regulatory Network Inference
Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene expression data despite its significant influence upon inferred results. This paper present...
Muhammad Shoaib B. Sehgal, Iqbal Gondal, Laurence ...