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Techniques for clustering gene expression data [PDF]
Computers in Biology and Medicine, 2008 Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile.
Kerr, Gráinne +3 more
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Bayesian biclustering of gene expression data [PDF]
BMC Genomics, 2008 Background Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions ...
Liu Jun S, Gu Jiajun
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On Differential Gene Expression Using RNA-Seq Data
Cancer Informatics, 2011 Motivation RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes.
Juhee Lee +4 more
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Gene Expression Databases and Data Mining [PDF]
BioTechniques, 2003 The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study.
Pascale Anderle +7 more
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Ensemble machine learning on gene expression data for cancer classification [PDF]
, 2003 Whole genome RNA expression studies permit systematic approaches to understanding the correlation between gene expression profiles to disease states or different developmental stages of a cell.
Tan, A C, Gilbert, D
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M-quantile regression analysis of temporal gene expression data [PDF]
, 2009 In this paper, we explore the use of M-regression and M-quantile coefficients to detect statistical differences between temporal curves that belong to different experimental conditions.
Vinciotti, V, Yu, K
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Gene expression data preprocessing [PDF]
Bioinformatics, 2003 Abstract Summary: We present an interactive web tool for preprocessing microarray gene expression data. It analyses the data, suggests the most appropriate transformations and proceeds with them after user agreement. The normal preprocessing steps include scale transformations, management of missing values, replicate handling, flat ...
Javier Herrero +2 more
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Error Distribution for Gene Expression Data [PDF]
Statistical Applications in Genetics and Molecular Biology, 2005 We present a new instance of Laplace's second Law of Errors and show how it can be used in the analysis of data from microarray experiments. This error distribution is shown to fit microarray expression data much better than a normal distribution. The use of this distribution in a parametric bootstrap leads to more powerful tests as we show that the t ...
Purdom, Elizabeth, Holmes, Susan P.
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Finding Groups in Gene Expression Data [PDF]
BioMed Research International, 2005 The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large‐scale, high‐throughput investigations of gene activity and have
Hand, DJ, Heard, NA
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Dynamic modeling of gene expression data [PDF]
Proceedings of the National Academy of Sciences, 2001 We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by ...
HOLTER N +4 more
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