Publication Name
Proceedings of the twenty-fourth annual conference on Neural Information Processing Systems (NIPS).
Publisher
Curran Associates
ISBN
9781617823800
Abstract
Conventional dynamic Bayesian networks (DBNs) are based on the
homogeneous Markov assumption, which is too restrictive
in many practical applications. Various approaches to relax the homogeneity
assumption have therefore been proposed in the last few years.
The present paper aims to
improve the flexibility of two recent versions of non-homogeneous
DBNs, which either (i) suffer from the need for data discretization,
or (ii) assume a time-invariant network structure.
Allowing the network structure to be fully flexible leads
to the risk of overfitting and inflated inference uncertainty though,
especially
in the highly topical field of systems biology, where
independent measurements tend to be sparse.
In the present paper we investigate three conceptually
different regularization schemes based
on inter-segment information sharing. We assess the performance
in a comparative evaluation study based on
simulated data. We compare the predicted segmentation of gene expression
time series obtained during embryogenesis in {\em Drosophila melanogaster}
with other state-of-the-art techniques. We conclude our evaluation with
an application to synthetic biology, where the objective is to predict
a known regulatory network of five genes in {\em Saccharomyces cerevisiae}.
Year
2011
Category
Book Chapter