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» A Parallel Algorithm for Gene Expressing Data Biclustering
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86
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BMCBI
2010
110views more  BMCBI 2010»
14 years 10 months ago
Discovering local patterns of co - evolution: computational aspects and biological examples
Background: Co-evolution is the process in which two (or more) sets of orthologs exhibit a similar or correlative pattern of evolution. Co-evolution is a powerful way to learn abo...
Tamir Tuller, Yifat Felder, Martin Kupiec
BMCBI
2010
132views more  BMCBI 2010»
14 years 10 months ago
Parallel multiplicity and error discovery rate (EDR) in microarray experiments
Background: In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statist...
Wayne Wenzhong Xu, Clay J. Carter
98
Voted
BMCBI
2010
139views more  BMCBI 2010»
14 years 10 months ago
A highly efficient multi-core algorithm for clustering extremely large datasets
Background: In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput t...
Johann M. Kraus, Hans A. Kestler
83
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BIBE
2007
IEEE
155views Bioinformatics» more  BIBE 2007»
15 years 4 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
81
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BIBE
2004
IEEE
120views Bioinformatics» more  BIBE 2004»
15 years 2 months ago
Identifying Projected Clusters from Gene Expression Profiles
In microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to ...
Kevin Y. Yip, David W. Cheung, Michael K. Ng, Kei-...