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BMCBI
2006
124views more  BMCBI 2006»
13 years 8 months ago
Predicting transcription factor binding sites using local over-representation and comparative genomics
Background: Identifying cis-regulatory elements is crucial to understanding gene expression, which highlights the importance of the computational detection of overrepresented tran...
Matthieu Defrance, Hélène Touzet
BMCBI
2005
122views more  BMCBI 2005»
13 years 7 months ago
GenClust: A genetic algorithm for clustering gene expression data
Background: Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designe...
Vito Di Gesù, Raffaele Giancarlo, Giosu&egr...
BMCBI
2007
207views more  BMCBI 2007»
13 years 8 months ago
Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clus
Background: The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell...
Nikhil R. Pal, Kripamoy Aguan, Animesh Sharma, Shu...
ICPR
2008
IEEE
14 years 2 months ago
A fuzzy c-means algorithm using a correlation metrics and gene ontology
A fuzzy c-means algorithm was adapted for analyzing microarray data. The adaptation consisted of initialization of fuzzy centroids using gene ontology information and the use of P...
Mingrui Zhang, Terry M. Therneau, Michael A. McKen...
BIODATAMINING
2008
178views more  BIODATAMINING 2008»
13 years 8 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