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» Evaluation of clustering algorithms for gene expression data
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BIBE
2007
IEEE
155views Bioinformatics» more  BIBE 2007»
15 years 3 months ago
Partial Mixture Model for Tight Clustering in Exploratory Gene Expression Analysis
Abstract—In this paper we demonstrate the inherent robustness of minimum distance estimator that makes it a potentially powerful tool for parameter estimation in gene expression ...
Yinyin Yuan, Chang-Tsun Li
KDD
2003
ACM
142views Data Mining» more  KDD 2003»
15 years 10 months ago
Mining phenotypes and informative genes from gene expression data
Mining microarray gene expression data is an important research topic in bioinformatics with broad applications. While most of the previous studies focus on clustering either gene...
Chun Tang, Aidong Zhang, Jian Pei
HICSS
2002
IEEE
127views Biometrics» more  HICSS 2002»
15 years 2 months ago
Interactive Visualization and Analysis for Gene Expression Data
Currently, the cDNA and genomic sequence projects are processing at such a rapid rate that more and more gene data become available. New methods are needed to efficiently and eff...
Chun Tang, Li Zhang, Aidong Zhang
BMCBI
2006
119views more  BMCBI 2006»
14 years 9 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs
65
Voted
CSB
2004
IEEE
164views Bioinformatics» more  CSB 2004»
15 years 1 months ago
Biclustering in Gene Expression Data by Tendency
The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Clustering is the most popular approach of analyzing gene expression data a...
Jinze Liu, Jiong Yang, Wei Wang 0010