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» Predicting Nucleolar Proteins Using Support-Vector Machines
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BMCBI
2007
207views more  BMCBI 2007»
14 years 9 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...
BMCBI
2006
105views more  BMCBI 2006»
14 years 9 months ago
CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks
Background: One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive unde...
Akira R. Kinjo, Ken Nishikawa
BMCBI
2006
143views more  BMCBI 2006»
14 years 9 months ago
IsoSVM - Distinguishing isoforms and paralogs on the protein level
Background: Recent progress in cDNA and EST sequencing is yielding a deluge of sequence data. Like database search results and proteome databases, this data gives rise to inferred...
Michael Spitzer, Stefan Lorkowski, Paul Cullen, Al...
ICMLA
2010
14 years 7 months ago
Smoothing Gene Expression Using Biological Networks
Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify inform...
Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
TIT
2002
164views more  TIT 2002»
14 years 9 months ago
On the generalization of soft margin algorithms
Generalization bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning syste...
John Shawe-Taylor, Nello Cristianini