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» Optimizing abstaining classifiers using ROC analysis
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BMCBI
2008
135views more  BMCBI 2008»
13 years 7 months ago
Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles
Background: DNA methylation patterns have been shown to significantly correlate with different tissue types and disease states. High-throughput methylation arrays enable large-sca...
Hailong Meng, Edward Lenn Murrelle, Guoya Li
BMCBI
2010
190views more  BMCBI 2010»
13 years 7 months ago
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification alg
Background: Data generated using `omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of...
Yu Guo, Armin Graber, Robert N. McBurney, Raji Bal...
MICCAI
2009
Springer
14 years 8 months ago
MKL for Robust Multi-modality AD Classification
We study the problem of classifying mild Alzheimer's disease (AD) subjects from healthy individuals (controls) using multi-modal image data, to facilitate early identification...
Chris Hinrichs, Vikas Singh, Guofan Xu, Sterlin...
BMCBI
2006
173views more  BMCBI 2006»
13 years 7 months ago
Kernel-based distance metric learning for microarray data classification
Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
Huilin Xiong, Xue-wen Chen
CAIP
2001
Springer
293views Image Analysis» more  CAIP 2001»
13 years 12 months ago
A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features
In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian est...
Zoltan Kato, Ting-Chuen Pong