23,152 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
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Three classes of algorithms to learn the structure of Bayesian networks from
data are common in the literature: constraint-based algorithms, which use
conditional independence tests to learn the dependence structure of the data;
score-based algorithms, which use goodness-of-fit scores as objective functions
to maximise; and hybrid algorithms that combine both approaches.
Constraint-based and score-based algorithms have been shown to learn the same
structures when conditional independence and goodness of fit are both assessed
using entropy and the topological ordering of the network is known (Cowell,
2001).
In this paper, we investigate how these three classes of algorithms perform
outside the assumptions above in terms of speed and accuracy of network
reconstruction for both discrete and Gaussian Bayesian networks. We approach
this question by recognising that structure learning is defined by the
combination of a statistical criterion and an algorithm that determines how the
criterion is applied to the data. Removing the confounding effect of different
choices for the statistical criterion, we find using both simulated and
real-world complex data that constraint-based algorithms are often less
accurate than score-based algorithms, but are seldom faster (even at large
sample sizes); and that hybrid algorithms are neither faster nor more accurate
than constraint-based algorithms. This suggests that commonly held beliefs on
structure learning in the literature are strongly influenced by the choice of
particular statistical criteria rather than just by the properties of the
algorithms themselves.Comment: 27 pages, 8 figure
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