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» Scaling-Up Support Vector Machines Using Boosting Algorithm
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BMCBI
2007
173views more  BMCBI 2007»
14 years 9 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
BMCBI
2007
117views more  BMCBI 2007»
14 years 9 months ago
Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers
Background: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis...
Xinan Yang, Xiao Sun
85
Voted
BMCBI
2010
134views more  BMCBI 2010»
14 years 9 months ago
Semi-automated screening of biomedical citations for systematic reviews
Background: Systematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature. Citation screening is a time-consuming and crit...
Byron C. Wallace, Thomas A. Trikalinos, Joseph Lau...
BMCBI
2004
140views more  BMCBI 2004»
14 years 9 months ago
What can we learn from noncoding regions of similarity between genomes?
Background: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to as...
Thomas A. Down, Tim J. P. Hubbard
96
Voted
ICASSP
2011
IEEE
14 years 1 months ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...