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ISNN
2005
Springer
14 years 2 months ago
Non-parametric Statistical Tests for Informative Gene Selection
This paper presents two non-parametric statistical test methods, called Kolmogorov-Smirnov (KS) and U statistic test methods, respectively, for informative gene selection of a tumo...
Jinwen Ma, Fuhai Li, Jianfeng Liu
BMCBI
2008
228views more  BMCBI 2008»
13 years 8 months ago
Adaptive diffusion kernel learning from biological networks for protein function prediction
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Liang Sun, Shuiwang Ji, Jieping Ye
BMCBI
2006
142views more  BMCBI 2006»
13 years 8 months ago
Improving the Performance of SVM-RFE to Select Genes in Microarray Data
Background: Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. ...
Yuanyuan Ding, Dawn Wilkins
BIBE
2001
IEEE
14 years 7 days ago
Gene Classification using Expression Profiles: A Feasibility Study
As various genome sequencing projects have already been completed or are near completion, genome researchers are shifting their focus from structural genomics to functional genomi...
Michihiro Kuramochi, George Karypis
TREC
2004
13 years 10 months ago
Identifying Relevant Full-Text Articles for GO Annotation Without MeSH Terms
Gene Ontology (GO) is a controlled vocabulary. Given a gene product, GO enables scientists to clearly and unambiguously describe specific molecular functions of the gene product, ...
Chih Lee, Wen-Juan Hou, Hsin-Hsi Chen