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» Combined Gene Selection Methods for Microarray Data Analysis
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BMCBI
2006
140views more  BMCBI 2006»
15 years 3 months ago
Feature selection using Haar wavelet power spectrum
Background: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical m...
Prabakaran Subramani, Rajendra Sahu, Shekhar Verma
JBI
2007
138views Bioinformatics» more  JBI 2007»
15 years 3 months ago
Towards knowledge-based gene expression data mining
ct 10 The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at 11 complementing microarray analysis with...
Riccardo Bellazzi, Blaz Zupan
ISNN
2005
Springer
15 years 8 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
2006
374views more  BMCBI 2006»
15 years 3 months ago
AMDA: an R package for the automated microarray data analysis
Background: Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and...
Mattia Pelizzola, Norman Pavelka, Maria Foti, Paol...
KDD
2002
ACM
145views Data Mining» more  KDD 2002»
16 years 3 months ago
Handling very large numbers of association rules in the analysis of microarray data
The problem of analyzing microarray data became one of important topics in bioinformatics over the past several years, and different data mining techniques have been proposed for ...
Alexander Tuzhilin, Gediminas Adomavicius