69 research outputs found
TriGen: A genetic algorithm to mine triclusters in temporal gene expression data
Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering
techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested.
Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to
be evaluated only under a subset of the conditions and not under all of them. However, this technique is not
appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at
several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that
take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine
datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host
response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in
subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations
extracted from the Gene Ontology.Ministerio de Ciencia y TecnologĂa TIN2011-28956-C00Ministerio de Ciencia y TecnologĂa TIN2009-13950Junta de AndalucĂa TIC-752
Analysis of regulatory network involved in mechanical induction of embryonic stem cell differentiation
Embryonic stem cells are conventionally differentiated by modulating specific growth factors in the cell culture media. Recently the effect of cellular mechanical microenvironment in inducing phenotype specific differentiation has attracted considerable attention. We have shown the possibility of inducing endoderm differentiation by culturing the stem cells on fibrin substrates of specific stiffness [1]. Here, we analyze the regulatory network involved in such mechanically induced endoderm differentiation under two different experimental configurations of 2-dimensional and 3-dimensional culture, respectively. Mouse embryonic stem cells are differentiated on an array of substrates of varying mechanical properties and analyzed for relevant endoderm markers. The experimental data set is further analyzed for identification of co-regulated transcription factors across different substrate conditions using the technique of bi-clustering. Overlapped bi-clusters are identified following an optimization formulation, which is solved using an evolutionary algorithm. While typically such analysis is performed at the mean value of expression data across experimental repeats, the variability of stem cell systems reduces the confidence on such analysis of mean data. Bootstrapping technique is thus integrated with the bi-clustering algorithm to determine sets of robust bi-clusters, which is found to differ significantly from corresponding bi-clusters at the mean data value. Analysis of robust bi-clusters reveals an overall similar network interaction as has been reported for chemically induced endoderm or endodermal organs but with differences in patterning between 2-dimensional and 3-dimensional culture. Such analysis sheds light on the pathway of stem cell differentiation indicating the prospect of the two culture configurations for further maturation. © 2012 Zhang et al
Unravelling the Yeast Cell Cycle Using the TriGen Algorithm
Analyzing microarray data represents a computational challenge
due to the characteristics of these data. Clustering techniques are
widely applied to create groups of genes that exhibit a similar behavior
under the conditions tested. Biclustering emerges as an improvement of
classical clustering since it relaxes the constraints for grouping allowing
genes to be evaluated only under a subset of the conditions and not under
all of them. However, this technique is not appropriate for the analysis of
temporal microarray data in which the genes are evaluated under certain
conditions at several time points. In this paper, we present the results of
applying the TriGen algorithm, a genetic algorithm that finds triclusters
that take into account the experimental conditions and the time points,
to the yeast cell cycle problem, where the goal is to identify all genes
whose expression levels are regulated by the cell cycle
CorrelationâBased Scatter Search for Discovering Biclusters from Gene Expression Data
Scatter Search is an evolutionary method that combines ex isting solutions to create new offspring as the wellâknown genetic algo rithms. This paper presents a Scatter Search with the aim of finding
biclusters from gene expression data. However, biclusters with certain
patterns are more interesting from a biological point of view. Therefore,
the proposed Scatter Search uses a measure based on linear correlations
among genes to evaluate the quality of biclusters. As it is usual in Scatter
Search methodology an improvement method is included which avoids
to find biclusters with negatively correlated genes. Experimental results
from yeast cell cycle and human B-cell lymphoma datasets are reported
showing a remarkable performance of the proposed method and measureMinisterio de Ciencia y TecnologĂa TIN2007-68084-C00Junta de AndalucĂa P07-TIC-0261
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