9,454 research outputs found
Application of new probabilistic graphical models in the genetic regulatory networks studies
This paper introduces two new probabilistic graphical models for
reconstruction of genetic regulatory networks using DNA microarray data. One is
an Independence Graph (IG) model with either a forward or a backward search
algorithm and the other one is a Gaussian Network (GN) model with a novel
greedy search method. The performances of both models were evaluated on four
MAPK pathways in yeast and three simulated data sets. Generally, an IG model
provides a sparse graph but a GN model produces a dense graph where more
information about gene-gene interactions is preserved. Additionally, we found
two key limitations in the prediction of genetic regulatory networks using DNA
microarray data, the first is the sufficiency of sample size and the second is
the complexity of network structures may not be captured without additional
data at the protein level. Those limitations are present in all prediction
methods which used only DNA microarray data.Comment: 38 pages, 3 figure
A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes
The influence of DNA cis-regulatory elements on a gene's expression has been
intensively studied. However, little is known about expressions driven by
trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are
recognized to be important in cancer as they influence multiple genes on a
global scale. The challenge in detecting trans-effects is mainly due to the
computational difficulty in detecting weak and sparse trans-acting signals
amidst co-occuring passenger events. We propose an integrative approach to
learn a sparse interaction network of DNA copy-number regions with their
downstream targets in a breast cancer dataset. Information from this network
helps distinguish copy-number driven from copy-number independent expression
changes on a global scale. Our result further delineates cis- and trans-effects
in a breast cancer dataset, for which important oncogenes such as ESR1 and
ERBB2 appear to be highly copy-number dependent. Further, our model is shown to
be efficient and in terms of goodness of fit no worse than other state-of the
art predictors and network reconstruction models using both simulated and real
data.Comment: Accepted at IEEE International Conference on Bioinformatics &
Biomedicine (BIBM 2010
Inferring causal relations from multivariate time series : a fast method for large-scale gene expression data
Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation
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