13,510 research outputs found
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Gene and protein networks are very important to model complex large-scale
systems in molecular biology. Inferring or reverseengineering such networks can
be defined as the process of identifying gene/protein interactions from
experimental data through computational analysis. However, this task is
typically complicated by the enormously large scale of the unknowns in a rather
small sample size. Furthermore, when the goal is to study causal relationships
within the network, tools capable of overcoming the limitations of correlation
networks are required. In this work, we make use of Bayesian Graphical Models
to attach this problem and, specifically, we perform a comparative study of
different state-of-the-art heuristics, analyzing their performance in inferring
the structure of the Bayesian Network from breast cancer data
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
Discovering Graphical Granger Causality Using the Truncating Lasso Penalty
Components of biological systems interact with each other in order to carry
out vital cell functions. Such information can be used to improve estimation
and inference, and to obtain better insights into the underlying cellular
mechanisms. Discovering regulatory interactions among genes is therefore an
important problem in systems biology. Whole-genome expression data over time
provides an opportunity to determine how the expression levels of genes are
affected by changes in transcription levels of other genes, and can therefore
be used to discover regulatory interactions among genes.
In this paper, we propose a novel penalization method, called truncating
lasso, for estimation of causal relationships from time-course gene expression
data. The proposed penalty can correctly determine the order of the underlying
time series, and improves the performance of the lasso-type estimators.
Moreover, the resulting estimate provides information on the time lag between
activation of transcription factors and their effects on regulated genes. We
provide an efficient algorithm for estimation of model parameters, and show
that the proposed method can consistently discover causal relationships in the
large , small setting. The performance of the proposed model is
evaluated favorably in simulated, as well as real, data examples. The proposed
truncating lasso method is implemented in the R-package grangerTlasso and is
available at http://www.stat.lsa.umich.edu/~shojaie.Comment: 12 pages, 4 figures, 1 tabl
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