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BMCBI
2008
104views more  BMCBI 2008»
13 years 5 months ago
Missing value imputation improves clustering and interpretation of gene expression microarray data
Background: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value i...
Johannes Tuikkala, Laura Elo, Olli Nevalainen, Ter...
BMCBI
2010
110views more  BMCBI 2010»
13 years 5 months ago
Missing value imputation for epistatic MAPs
Background: Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The dat...
Colm Ryan, Derek Greene, Gerard Cagney, Padraig Cu...
AIME
2009
Springer
13 years 9 months ago
The Role of Biomedical Dataset in Classification
In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. We quantify the complexity of a biomedical dataset using five complex...
Ajay Kumar Tanwani, Muddassar Farooq
BMCBI
2008
193views more  BMCBI 2008»
13 years 5 months ago
Missing value imputation for microarray gene expression data using histone acetylation information
Background: It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile ...
Qian Xiang, Xianhua Dai, Yangyang Deng, Caisheng H...
BMCBI
2004
113views more  BMCBI 2004»
13 years 5 months ago
Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering
Background: Microarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray dat...
Alexandre G. de Brevern, Serge A. Hazout, Alain Ma...