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GPEM
2002
95views more  GPEM 2002»
13 years 4 months ago
On Appropriate Adaptation Levels for the Learning of Gene Linkage
A number of algorithms have been proposed aimed at tackling the problem of learning "Gene Linkage" within the context of genetic optimisation, that is to say, the problem...
James Smith
BMCBI
2007
194views more  BMCBI 2007»
13 years 5 months ago
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Xin Zhao, Leo Wang-Kit Cheung
BMCBI
2005
100views more  BMCBI 2005»
13 years 4 months ago
Speeding disease gene discovery by sequence based candidate prioritization
Background: Regions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is...
Euan A. Adie, Richard R. Adams, Kathryn L. Evans, ...
BMCBI
2010
179views more  BMCBI 2010»
13 years 5 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...
BMCBI
2006
173views more  BMCBI 2006»
13 years 4 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