13,694 research outputs found
Inference of Temporally Varying Bayesian Networks
When analysing gene expression time series data an often overlooked but
crucial aspect of the model is that the regulatory network structure may change
over time. Whilst some approaches have addressed this problem previously in the
literature, many are not well suited to the sequential nature of the data. Here
we present a method that allows us to infer regulatory network structures that
may vary between time points, utilising a set of hidden states that describe
the network structure at a given time point. To model the distribution of the
hidden states we have applied the Hierarchical Dirichlet Process Hideen Markov
Model, a nonparametric extension of the traditional Hidden Markov Model, that
does not require us to fix the number of hidden states in advance. We apply our
method to exisiting microarray expression data as well as demonstrating is
efficacy on simulated test data
The EM Algorithm and the Rise of Computational Biology
In the past decade computational biology has grown from a cottage industry
with a handful of researchers to an attractive interdisciplinary field,
catching the attention and imagination of many quantitatively-minded
scientists. Of interest to us is the key role played by the EM algorithm during
this transformation. We survey the use of the EM algorithm in a few important
computational biology problems surrounding the "central dogma"; of molecular
biology: from DNA to RNA and then to proteins. Topics of this article include
sequence motif discovery, protein sequence alignment, population genetics,
evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Extending boolean regulatory network models with answer set programming
Because of their simplicity, boolean networks are a popular formalism to model gene regulatory networks. However, they have their limitations, including their inability to formally and unambiguously define network behaviour, and their lack of the possibility to model meta interactions, i.e., interactions that target other interactions. In this paper we develop an answer set programming (ASP) framework that supports threshold boolean network semantics and extends it with the capability to model meta interactions. The framework is easy to use but sufficiently flexible to express intricate interactions that go beyond threshold network semantics as we illustrate with an example of a Mammalian cell cycle network. Moreover, readily available answer set solvers can be used to find the steady states of the network
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